US20060173371A1 - Method and apparatus for determining alternans data of an ECG signal - Google Patents

Method and apparatus for determining alternans data of an ECG signal Download PDF

Info

Publication number
US20060173371A1
US20060173371A1 US11/393,575 US39357506A US2006173371A1 US 20060173371 A1 US20060173371 A1 US 20060173371A1 US 39357506 A US39357506 A US 39357506A US 2006173371 A1 US2006173371 A1 US 2006173371A1
Authority
US
United States
Prior art keywords
feature
beat
matrix
points
principal component
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US11/393,575
Inventor
Joel Xue
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
GE Medical Systems Information Technologies Inc
Original Assignee
GE Medical Systems Information Technologies Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by GE Medical Systems Information Technologies Inc filed Critical GE Medical Systems Information Technologies Inc
Priority to US11/393,575 priority Critical patent/US20060173371A1/en
Publication of US20060173371A1 publication Critical patent/US20060173371A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346Analysis of electrocardiograms
    • A61B5/349Detecting specific parameters of the electrocardiograph cycle

Definitions

  • the present invention relates to cardiology, and more specifically to methods and apparatus for determining alternans data of an electrocardiogram (“ECG”) signal.
  • ECG electrocardiogram
  • Alternans are a subtle beat-to-beat change in the repeating pattern of an ECG signal.
  • TWA T-wave alternans
  • an ECG signal typically has an amplitude measured in millivolts
  • an alternans pattern of variation with an amplitude on the order of a microvolt may be clinically significant. Accordingly, an alternans pattern of variation is typically too small to be detected by visual inspection of the ECG signal in its typical recorded resolution. Instead, digital signal processing and quantification of the alternans pattern of variation is necessary. Such signal processing and quantification of the alternans pattern of variation is complicated by the presence of noise and time shift of the alternans pattern of variation to the alignment points of each beat, which can be caused by limitation of alignment accuracy and/or physiological variations in the measured ECG signal.
  • Current signal processing techniques utilized to detect TWA patterns of variation in an ECG signal include spectral domain methods and time domain methods.
  • one or more embodiments of the invention provide methods and apparatus for determining alternans data of an ECG signal.
  • the method can include determining at least one value representing at least one morphology feature of each beat of the ECG signal and generating a set of data points based on a total quantity of values and a total quantity of beats.
  • the data points can each include a first value determined using a first mathematical function and a second value determined using a second mathematical function.
  • the method can also include separating the data points into a first group of points and a second group of points and generating a feature map by plotting the first group of points and the second group of points in order to assess an alternans pattern of variation.
  • FIG. 1 is a schematic diagram illustrating a cardiac monitoring system according to the invention.
  • FIG. 2 illustrates an ECG signal
  • FIG. 3 is a flow chart illustrating one embodiment of a method of the invention.
  • FIG. 4 illustrates a maximum morphology feature
  • FIG. 5 illustrates a minimum morphology feature
  • FIG. 6 illustrates an area morphology feature
  • FIG. 7 illustrates another area morphology feature.
  • FIG. 8 illustrates a further area morphology feature.
  • FIG. 9 illustrates still another area morphology feature.
  • FIG. 10 illustrates a plurality of beats, each beat being divided into a plurality of portions.
  • FIG. 11 illustrates a window establishing a size of one of the plurality of portions of FIG. 10 .
  • FIG. 12 illustrates a feature matrix
  • FIG. 13 illustrates a decomposition of the feature matrix of FIG. 12 as generated by a principal component analysis.
  • FIG. 14 illustrates a plot of values of data corresponding to values representative of a morphology feature.
  • FIG. 15 illustrates a determination of difference features using the values plotted in FIG. 14 .
  • FIG. 16 illustrates another determination of difference features using the values plotted in FIG. 14 .
  • FIG. 17 illustrates a further determination of a difference feature using the values plotted in FIG. 14 .
  • FIG. 18 illustrates a feature map of first and second groups of points generated using values of a vector of data.
  • FIG. 19 illustrates a feature map generated using values of a vector of data generated by performing a principal component analysis on a feature matrix including the vector of data utilized to generate the feature map of FIG. 18 .
  • FIG. 20 illustrates a feature map of first and second groups of points generated using a first mathematical function and a second mathematical function.
  • FIG. 21 illustrates a feature map of third and fourth groups of points generated using a third mathematical function and a fourth mathematical function.
  • FIG. 22 illustrates a feature map of fifth and sixth groups of points generated using a fifth mathematical function and the sixth mathematical function.
  • FIG. 23 illustrates a distance between a first center point of a first group of points and a second center point of a second group of points each plotted to form a feature map.
  • FIG. 24 illustrates a spectral graph generated using values of a vector of data.
  • FIG. 25 illustrates a spectral graph generated using values of a vector of data generated by performing a principal component analysis on a feature matrix including the vector of data utilized to generate the spectral graph of FIG. 24 .
  • embodiments of the invention include both hardware and electronic components or modules that, for purposes of discussion, may be illustrated and described as if the majority of the components were implemented solely in hardware.
  • the electronic based aspects of the invention may be implemented in software.
  • a plurality of hardware and software based devices, as well as a plurality of different structural components may be utilized to implement the invention.
  • the specific mechanical configurations illustrated in the drawings are intended to exemplify embodiments of the invention and that other alternative mechanical configurations are possible.
  • FIG. 1 illustrates a cardiac monitoring system 10 according to some embodiments of the invention.
  • the cardiac monitoring system 10 can acquire ECG data, can process the acquired ECG data to determine alternans data, and can output the alternans data to a suitable output device (e.g., a display, a printer, and the like).
  • a suitable output device e.g., a display, a printer, and the like.
  • alternans data includes TWA data, or any other type of alternans data that is capable of being determined using one or more embodiments of the invention.
  • the cardiac monitoring system 10 can acquire ECG data using a data acquisition module.
  • ECG data can be acquired from other sources (e.g., from storage in a memory device or a hospital information system).
  • the data acquisition module can be coupled to a patient by an array of sensors or transducers which may include, for example, electrodes coupled to the patient for obtaining an ECG signal.
  • the electrodes can include a right arm electrode RA; a left arm electrode LA; chest electrodes V 1 , V 2 , V 3 , V 4 , V 5 and V 6 ; a right leg electrode RL; and a left electrode leg LL for acquiring a standard twelve-lead, ten-electrode ECG.
  • alternative configurations of sensors or transducers e.g., less than ten electrodes can be used to acquire a standard or non-standard ECG signal.
  • the ECG signal can include [G] beats including beat-one B 1 through beat-[G] B G where [G] is a value greater than one.
  • [G] is a value greater than one.
  • a capital letter in brackets represents a quantity, and a capital letter without brackets is a reference character (similar to a typical reference numeral).
  • the data acquisition module can include filtering and digitization components for producing digitized ECG data representing the ECG signal.
  • the ECG data can be filtered using low pass and baseline wander removal filters to remove high frequency noise and low frequency artifacts.
  • the ECG data can, in some embodiments, be filtered by removing arrhythmic beats from the ECG data and by eliminating noisy beats from the ECG data.
  • the cardiac monitoring system 10 can include a processor and a memory associated with the processor.
  • the processor can execute a software program stored in the memory to perform a method of the invention as illustrated in FIG. 3 .
  • FIG. 3 is a flow chart of a method of the invention used to determine and display alternans data of an ECG signal.
  • the cardiac monitoring system 10 is described herein as including a single processor that executes a single software program, it should be understood that the system can include multiple processors, memories, and/or software programs. Further, the method of the invention illustrated in FIG. 3 can be performed manually or using other systems.
  • the processor can receive (at 100 ) ECG data representing an ECG signal.
  • the acquired ECG data can be received (e.g., from a patient in real-time via the data acquisition module or from storage in a memory device) and can be processed as necessary.
  • the ECG data can represent continuous and/or non-continuous beats of the ECG signal.
  • the ECG data, or a portion thereof can be parsed into a plurality of data sets.
  • Each data set can represent a portion of a respective beat B of the ECG signal (e.g., the T-wave portion of a respective beat B of the ECG signal), a portion of a respective odd or even median beat of the ECG signal, a portion of a respective odd or even mean beat of the ECG signal, and the like.
  • the parsed data sets can be saved in an array (e.g., a waveform array). In other embodiments, the ECG data can be saved in a single data set, or alternatively, saved in multiple data sets.
  • the processor can determine (at 102 ) a quantity [C] of values W representing a quantity [D] of morphology features F of a beat B (e.g., beat-one B 1 ) of a quantity [G] beats, where [C] and [D] are each a quantity greater than or equal to one.
  • a single value W is determined for each morphology feature F (i.e., the quantity of [C] is equal to the quantity of [D]).
  • multiple values W are determined for a single morphology feature F and/or a single value W is determined for multiple morphology features F.
  • Determining a quantity [C] of values W representing a quantity [D] of morphology features F can be repeated for a quantity [H ⁇ 1] of beats of the quantity [G] of beats represented in the collected ECG data where a quantity [H] is greater than or equal to one and less than or equal to the quantity [G].
  • any morphology features F of the beats B can be determined.
  • FIGS. 4-9 illustrate some examples of such morphology features F.
  • FIG. 4 illustrates a maximum morphology feature (i.e., the maximum value of the data set representing the T-wave portion of a respective beat).
  • FIG. 5 illustrates a minimum morphology feature (i.e., the minimum value of the data set representing the T-wave portion of a respective beat).
  • FIG. 6 illustrates an area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the minimum value of the data set).
  • FIG. 4 illustrates a maximum morphology feature (i.e., the maximum value of the data set representing the T-wave portion of a respective beat).
  • FIG. 5 illustrates a minimum morphology feature (i.e., the minimum value of the data set representing the T-wave portion of a respective beat).
  • FIG. 6 illustrates an area
  • FIG. 7 illustrates another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the maximum value of the data set and a point of the data set representing the maximum up-slope of the curve).
  • FIG. 8 illustrates still another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the minimum value of the data set and a point of the data set representing the maximum down-slope of the curve).
  • FIG. 8 illustrates still another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the minimum value of the data set and a point of the data set representing the maximum down-slope of the curve).
  • FIG. 9 illustrates yet another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by a point of the data set representing the maximum up-slope of the curve and a point of the data set representing the maximum down-slope of the curve).
  • area morphology feature i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by a point of the data set representing the maximum up-slope of the curve and a point of the data set representing the maximum down-slope of the curve.
  • Other types of maximum, minimum, and area morphology features can also be used.
  • morphology features that can be used include amplitude morphology features (e.g., an amplitude of a point representing the maximum down-slope of the curve formed by the data set representing the T-wave portion of a respective beat) and slope morphology features (e.g., a maximum positive slope of the curve formed by the data set representing the T-wave portion of a respective beat).
  • amplitude morphology features e.g., an amplitude of a point representing the maximum down-slope of the curve formed by the data set representing the T-wave portion of a respective beat
  • slope morphology features e.g., a maximum positive slope of the curve formed by the data set representing the T-wave portion of a respective beat.
  • mathematical model morphology features obtained by determining values representing a mathematical model of the curve formed by the data set representing the T-wave portion of a respective beat using, for example, a Gaussian function model, a power of Cosine function model, and/or a bell function model.
  • a further example is time interval morphology features (e.g., a time interval between a maximum value and a minimum value of the data set representing a T-wave portion of a respective beat).
  • shape correlation morphology features obtained by determining a value representing a shape correlation of the curve formed by the data set representing the T-wave portion of a respective beat using, for example, a cross-correlation method and/or an absolute difference correlation method.
  • ratio morphology features e.g., a ST:T ratio. Any other suitable morphology feature can be used in other embodiments of the invention.
  • the morphology feature can be determined using values of the data set(s) of the ECG data.
  • the morphology features can be determined using values representing the values of the data set(s) of the ECG data (e.g., a morphology feature of the first derivative of the curve formed by a respective data set).
  • Morphology features can be determined using an entire parsed data set as illustrated in FIGS. 4-9 , or alternatively, using a portion thereof as illustrated in FIGS. 10 and 11 .
  • each of the beats B can be divided up in a plurality of portions. The center of each portion can be defined by a vertical divider line.
  • a window can be established to define the size of the portion.
  • the window can include a single value of the data set (e.g., a value representing the point where the divider line crosses the curve formed by the data set), or values of the data set representing any number of points adjacent the intersection of the curve and the divider line.
  • the processor can generate (at 104 ) a feature matrix.
  • matrix includes any table of values.
  • the generated feature matrix can include a quantity [C] of values W representing each of the quantity [D] of morphology features F for each of the quantity [H] of beats B (i.e., the feature matrix includes a quantity [C] ⁇ [H] of values W).
  • Each value W can directly represent the determined morphology feature F (e.g., the actual value of the determined area morphology feature), or can indirectly represent the determined morphology feature (e.g., a normalized value of the determined area morphology feature).
  • the feature matrix A can include [C] columns and [H] rows.
  • the feature matrix A can use the columns to represent the quantity [D] of morphology features F (i.e., each column includes a quantity [H] of values W of the same morphology feature as determined for each of the quantity [H] of beats B), and the rows to represent the beats B (i.e., each row includes a quantity [C] of values representing the quantity [D] of morphology features for each of the quantity [H] of beats).
  • the values W of the morphology features F can be represented in the illustrated feature matrix A using the notation W IBJ and F I B J where I is a value between one and [C], the quantity of [C] being equal to the quantity of [D], and J is a value between one and [H].
  • the feature matrix A can be arranged in other suitable manners.
  • the values W representing the morphology features F can be saved for later processing.
  • the processor can preprocess (at 106 ) the feature matrix A.
  • a principal component analysis (PCA) can be performed on the feature matrix A.
  • PCA involves a multivariate mathematical procedure known as an eigen analysis which rotates the data to maximize the explained variance of the feature matrix A.
  • a set of correlated variables are transformed into a set of uncorrelated variables which are ordered by reducing variability, the uncorrelated variables being linear combinations of the original variables.
  • PCA is used to decompose the feature matrix A into three matrices, as illustrated in FIG. 13 .
  • the three matrices can include a matrix U, a matrix S, and a matrix V.
  • the matrix U can include the principal component vectors (e.g., the first principal component vector u 1 , the second principal component vector u 2 , . . . , the pth principal component vector u p ).
  • the principal component vectors are also known as eigen vectors.
  • the first principal component vector u 1 can represent the most dominant variance vector (i.e., the first principal component vector u 1 represents the largest beat-to-beat variance), the second principal component vector u 2 can represent the second most dominant variance vector, and so on.
  • the S Matrix can include the principal components (e.g., the first principal component S 1 , the second principal component S 2 , . . . , the pth principal component S p ).
  • the first principal component S 1 can account for as much of the variability in the data as possible, and each succeeding principal component S can account for as much of the remaining variability as possible.
  • the first principal component S 1 can be used to determine alternans data (e.g., the square-root of the first PCA component S 1 can provide an estimation of the amplitude of the most dominant alternans pattern of variation).
  • the second principal component S 2 and the third principal component S 3 can also provide useful alternans data.
  • the matrix V is generally known as the parameter matrix.
  • the matrix V can be raised to a power of T.
  • the preprocessing of the feature matrix A can include other types of mathematical analyses.
  • the robustness of the preprocessing of the feature matrix A can be enhanced by increasing the quantity of [H] as the quantity of [D] increases.
  • an increase in the number of morphology features F represented in the feature matrix A generally requires a corresponding increase in the number of beats B for which the morphology features F are being determined;
  • the correspondence between the quantities of [D] and [H] is often based on the dependency between each of the [D] morphology features F.
  • the quantity of [H] is greater than or equal to 32 and less than or equal to 128. In other embodiments, the quantity of [H] is less than 32 or greater than 128.
  • the value of [H] is adaptively changed in response to a corresponding change in the level of noise in the measured ECG signal.
  • the processor can determine (at 108 ) [E] points L using data corresponding to at least some of the values W, [E] being a quantity greater than or equal to one.
  • the data corresponding to the values W can include at least one value W, at least one value of a principal component vector (e.g., the first principal component vector u 1 ), and/or at least one value of any other data that corresponds to the values W.
  • Each point L can include a first value (e.g., one of an X-value and a Y-value) determined using a first mathematical function Feature(beat+[N]), and a second value (e.g., the other of the X-value and the Y-value) determined using a second mathematical function Feature(beat), [N] being a quantity greater than or equal to one.
  • Each of the first and second values of the points L represents a feature of the data corresponding to the values W.
  • the feature is a difference feature Q (i.e., the difference in amplitude between two values of the data corresponding to the values W as specified by the respective mathematical function).
  • the first and second values of the points L can represent another difference features (e.g., an absolute difference feature, a normalized difference feature, a square-root difference feature, and the like), or any other mathematically-definable feature of the data corresponding to the values W.
  • the feature can include a value feature where the feature is equal to a specified value of the data corresponding to the determined values W.
  • Equations 1 and 2 shown below define an example of the mathematical functions Feature(beat+[N]) and Feature(beat), respectively.
  • the first values of the points L determined using the mathematical function Feature(beat+[N]) can represent a difference feature Q K+[N] and the second values of the points L determined using the mathematical function Feature(beat) can represent the difference feature Q K , where K is a value equal to a beat (i.e., the beat for which the respective mathematical function is being used to determine either the first or second value of a point L).
  • Feature(beat+1) W (beat+2)
  • ⁇ W (beat+1) Q K+1 [e3]
  • the offset between the difference feature Q K+[N] and the difference feature Q K is dependent on the value of [N].
  • the second value of the point L is determined by finding the difference between the value W of the next beat B I+1 and the value W of the current beat B I .
  • the first value of the point L is determined by finding the difference between the value W of the fourth next beat B I+4 and the value W of the second next beat B I+2
  • the second value of the point L is determined by finding the difference between the value W of the second next beat B I+2 and the value W of the current beat B I
  • the first value of the point L is determined by finding the difference between the value W of the sixth next beat B I+6 and the value W of the third next beat B I+3
  • the second value of the point L is determined by finding the difference between the value W of the third next beat B I+3 and the value W of the current beat B I .
  • the first values of the points L determined using the first mathematical function Feature(beat+[N]) are offset relative to the second values of the points L determined using the second mathematical function Feature(beat) by a factor of [N].
  • the first mathematical function Feature(beat+[N]) determines Feature(2) . . . Feature(Z+1) for beat-one B 1 through beat-(Z) B Z
  • the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B 1 through beat-(Z) B Z
  • the “Beat” column can represent respective beats B of the ECG signal and the “Feature Value” column can represent a value W of a morphology feature F of the corresponding respective beat B (e.g., an area morphology feature).
  • the points L can be generated using values of other data corresponding to the determined values W.
  • FIG. 14 illustrates a plot of the feature values from Tables 1-3 for beat-one B 1 through beat-seven B 7 where each peak and each valley of the plot can represent a respective feature value W (e.g., value-one W 1 which represents beat-one B 1 , value-two W 2 which represents beat-two B 2 , . . . , value-seven W 7 which represents beat-seven B 7 ).
  • W e.g., value-one W 1 which represents beat-one B 1 , value-two W 2 which represents beat-two B 2 , . . . , value-seven W 7 which represents beat-seven B 7 ).
  • the seven values i.e., value-one W 1 through value-seven W 7
  • six difference features i.e., difference feature-one Q 1 through difference feature-six Q 6 ).
  • the first mathematical function generates difference feature-two Q 2 through difference feature-six Q 6 for beat-one B 1 through beat-five B 5 , respectively, using the seven values
  • the second mathematical function generates difference feature-one Q 1 through difference feature-six Q 6 for beat-one B 1 through beat-six B 6 , respectively, using the seven values.
  • the difference feature Q is illustrated in FIG. 15 as dotted-line arrows extending between two specified values of the plot of FIG. 14 .
  • difference feature-three Q 3 i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-two B 2
  • the difference can be found between value-four W 4 which represents beat-four B 4 and value-three W 3 which represents beat-three B 3 .
  • difference feature-six Q 6 i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-two B 5
  • second value of the point L as determined by the second mathematical function Feature(beat) for beat-six B 6 the difference can be found between value-four W 7 which represents beat-seven B 7 and value-six W 6 which represents beat-six B 6 .
  • the seven values i.e., value-one W 1 through value-seven W 7
  • the five difference features i.e., difference feature-one Q 1 through difference feature-five Q 5 ).
  • the first mathematical function generates difference feature-three Q 3 through difference feature-five Q 5 for beat-one B 1 through beat-three B 3 , respectively, using the seven values
  • the second mathematical function generates difference feature-one Q 1 through difference feature-five Q 5 for beat-one B 1 through beat-five B 5 , respectively, using the seven values.
  • the difference feature Q is illustrated in FIG. 16 as dotted-line arrows extending between two specified values of the plot of FIG. 14 .
  • difference feature-three Q 3 i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-one B 1
  • the difference can be found between value-five W 5 which represents beat-five B 5 and value-three W 3 which represents beat-three B 3 .
  • difference feature-five Q 5 i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-three B 3
  • second value of the point L as determined by the second mathematical function Feature(beat) for beat-five B 5 the difference can be found between value-four W 7 which represents beat-seven B 7 and value-five W 5 which represents beat-five B 5 .
  • the seven values i.e., value-one W 1 through value-seven W 7
  • the seven values generate four difference features (i.e., difference feature-one Q 1 through difference feature-four Q 4 ).
  • the first mathematical function generates difference feature-four Q 4 for beat-four B 4 using the seven values
  • the second mathematical function generates difference feature-one Q 1 through difference feature-four Q 4 for beat-one B 1 through beat-four B 4 , respectively, using the seven values.
  • the difference feature Q is illustrated in FIG. 17 as dotted-line arrows extending between two specified values of the plot of FIG. 14 .
  • difference feature-three Q 4 i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-one B 1
  • the difference can be found between value-seven W 7 which represents beat-seven B 7 and value-four W 4 which represents beat-four B 4 .
  • each point L can be assigned to a respective group (e.g., group A or group B).
  • the points L representing each odd beat e.g., beat-one B 1 , beat-three B 3 , . . . , beat-eleven B 11
  • first group i.e., group A
  • second group i.e., group B
  • the points L can be assigned to group A and group B in this manner to represent a proposed odd-even alternans pattern of variation (i.e., ABAB . . . ). In other embodiments, the points L can be alternatively assigned to groups to represent other proposed alternans patterns of variation (e.g., AABBAABB . . . , AABAAB . . . , and the like).
  • the feature map provides a visual indication of the divergence of the two groups of points, and thus the existence of a significant alternans pattern of variation. If there is a significant ABAB . . . alternans pattern of variation, the two groups of points will show separate clusters on the feature map (for example, as shown in FIGS. 20 and 22 ). If there is not a significant ABAB . . . alternans pattern of variation, the feature map will illustrate a more random pattern of points from the two groups (for example, as shown in FIG. 21 ).
  • FIGS. 18 and 19 illustrate two examples of feature maps.
  • the [E] points plotted to generate the feature maps of FIGS. 18 and 19 were determined using ECG data representative of an ECG signal having a 5 microvolt TWA pattern of variation, 20 microvolts of noise, and 20 milliseconds of offset, where [H] is equal to 128.
  • the first and second groups of points can be distinguished by the markers utilized to represent the points of the group (i.e., the first group of points, group A, can include asterisks shaped markers, and the second group of points, group B, can include round markers). Lines can be used to connect sequential markers of each group (e.g., for group A, point-two P 2A can be connected to each of point-one P 1A and point-three P 3A by lines).
  • the feature map of FIG. 18 illustrates a plot of points determined using values directly from the feature matrix A (i.e., the feature matrix A was not preprocessed using a principal component analysis or other mathematical analysis). As illustrated in FIG. 18 , the points of the first and second groups are intermixed (i.e., the feature map illustrates a random pattern of the points from the two groups). Accordingly, the feature map of FIG. 18 does not illustrate the presence of a significant divergence of the two groups of points, and thus, does not indicate the existence of a significant alternans pattern of variation.
  • the feature map of FIG. 19 illustrates a plot of points determined using values of a first principal vector u 1 .
  • the first principal vector u 1 is a result of a principal component analysis performed on the same feature matrix A from which the values used to determine the points L plotted in FIG. 18 were obtained.
  • the first and second groups of points are partially overlapped, the first group of points is primarily positioned in the upper-left quadrant of the feature map and the second group of points is primarily positioned in the lower-right quadrant of the feature map. Accordingly, the feature map of FIG. 19 appears to illustrate the presence of a significant divergence of the two groups of points, and thus, a significant alternans pattern of variation may exist.
  • FIGS. 18 and 19 illustrate the same ECG data
  • the feature map of FIG. 19 indicates the existence of an alternans pattern of variation, while the feature map of FIG. 18 does not.
  • the effect of noise and time shift in the measured ECG signal on the determined alternans data is clearly indicated by the feature maps of FIGS. 18 and 19 .
  • Preprocessing the feature matrix A increases the robustness of the determination of alternans data by limiting the effect of noise and time shift in the measured ECG signal.
  • the display of multiple feature maps can further verify the existence of a significant alternans pattern of variation for the proposed alternans pattern of variation (e.g., a ABAB . . . alternans pattern of variation).
  • the divergence of the first and second groups of points in the feature maps of FIGS. 20 and 22 in combination with the lack of divergence of the first and second groups of points in the feature map of FIG. 21 provides visual evidence that the proposed ABAB . . . alternans pattern of variation is correct.
  • the ECG signal represented by the values used to determine the points for the feature maps does not represent the proposed ABAB . . . alternans pattern of variation.
  • the ECG signal can include a different alternans pattern of variation. Reassignment of the [E] points to different groups can be used to test a different proposed alternans pattern of variation.
  • the processor can statistically analyze the data plotted in the feature map.
  • the feature map provides a visual indication of the existence of a significant alternans pattern of variation
  • the feature map does not provide a quantitative measure of the confidence level of the alternans pattern of variation.
  • the data plotted in the feature map, or similar types of data that are not plotted in a feature map can be statistically analyzed to provide such quantitative measures of the confidence level of the alternans pattern of variation.
  • a paired T-test can be performed on the first and second groups of points.
  • a paired T-test is a statistical test which is performed to determine if there is a statistically significant difference between two means.
  • the confidence level is increased (i.e., a significant alternans pattern of variation exists) when the p-value is less than 0.001.
  • other suitable threshold levels can be established.
  • a cluster analysis (e.g., a fuzzy cluster analysis or a K-mean cluster analysis) can be performed on the [E] points to determine a first cluster of points and a second cluster of points.
  • the cluster analysis can also generate a first center point for the first cluster and a second center point for the second cluster.
  • the first and second clusters of points can be compared with the first and second groups of points, respectively.
  • a determination can be made of the number of clustered points that match the corresponding grouped points.
  • point-one L 1 and point-two L 2 are clustered in the first cluster
  • point-three L 3 and point-four L 4 are clustered in the second cluster
  • point-one L 1 , point-two L 2 , and point-three L 3 can be grouped in the first group
  • point-four L 4 can be grouped in the second group.
  • Clustered point-three L 3 does not correspond to grouped point-three L 3 , thereby resulting in a 75% confidence level.
  • the confidence level can represent the percentage of clustered points that match the corresponding grouped points.
  • a confidence level about 90% can be a high confidence level
  • a confidence level between 60% and 90% can be a medium confidence level
  • a confidence level below 60% can be a low confidence level.
  • the thresholds for the high, medium, and/or low confidence levels can be other suitable ranges of percentages or values.
  • the processor can determine (at 114 ) an estimate of an amplitude of the alternans pattern of variation.
  • the square-root of a principal component e.g., the first principal component S 1
  • a distance can be determined between a first center point of a first group of points and a second center point of a second group of points.
  • the center points can include the center points of the first and second groups of points A and B as determined using a mathematical analysis (e.g., by taking the mean or median of the values of the points for each respective group), the center points provided by the Paired T-test, the center points provided by the cluster analysis, or any other determined center points that represent the ECG data.
  • a mathematical analysis e.g., by taking the mean or median of the values of the points for each respective group
  • the center points provided by the Paired T-test the center points provided by the cluster analysis, or any other determined center points that represent the ECG data.
  • FIG. 23 illustrates a distance measurement between the first and second center points.
  • the distance can be determined using Equation 9 shown below, where the first center point includes an X-value X 1 and a Y-value Y 1 and the second center point includes an X-value X 2 and a Y-value Y 2 .
  • Amplitude ESTIMATE ⁇ square root over (( X 1 ⁇ X 2 ) 2 +( Y 1 ⁇ Y 2 ) 2 ) ⁇ [e9]
  • the amplitude of the alternans pattern of variation often depends on the [D] morphology features used to determine the values W. Accordingly, the estimated amplitude is generally not an absolute value that can be compared against standardized charts. However, comparisons can be generated for estimated amplitudes of alternans patterns of variation based on the morphology features F that are determined and the processing step that is used.
  • the processor can report (at 116 ) alternans data to a caregiver and/or the processor can store the alternans data.
  • the alternans data e.g., the feature maps, the estimated amplitudes of the alternans pattern of variation, the confidence level of the alternans pattern of variation, the uncertainty level of the alternans pattern of variation, the p-value of the alternans pattern of variation, and the like
  • can be reported using any suitable means e.g., output to a suitable output device such as a display, a printer, and the like).
  • the processor can plot (at 118 ) a spectral graph using values resulting from preprocessing the feature matrix (e.g., the values of the first principal component vector u 1 ).
  • FIGS. 24 and 25 illustrate two examples of spectral graphs. The values used to generate the spectral graphs of both FIGS. 24 and 25 were determined using ECG data representative of an ECG signal having a 5 microvolt TWA pattern of variation, 20 microvolts of noise, and 20 milliseconds of offset, where [H] is equal to 128.
  • FIG. 24 illustrates a spectral graph generated using values directly from the feature matrix A (i.e., the feature matrix A was not preprocessed using a principal component analysis or other mathematical analysis). As illustrated in FIG. 24 , the spectral graph does not include a dominant frequency at half of the beat sample frequency, but instead includes a number of frequency spikes having varying amplitudes. Accordingly, the spectral graph of FIG. 24 does not indicate the existence of a significant alternans pattern of variation.
  • FIG. 25 illustrates a spectral graph generated using values of a first principal vector u 1 . The first principal vector u 1 is a result of a principal component analysis performed on the same feature matrix A from which the values used to generate the spectral graph of FIG. 24 were obtained.
  • FIG. 24 illustrates a spectral graph generated using values directly from the feature matrix A (i.e., the feature matrix A was not preprocessed using a principal component analysis or other mathematical analysis). As illustrated in FIG. 24 , the spectral graph does not
  • the spectral graph of FIG. 25 illustrates a single frequency spike at half of the beat sample frequency. Accordingly, unlike the spectral graph of FIG. 24 , the spectral graph of FIG. 25 appears to illustrate the presence of a significant alternans pattern of variation. The effect of noise and time shift in the measured ECG signal on the determined alternans data is indicated by the spectral graphs of FIGS. 24 and 25 . Preprocessing the feature matrix A increases the robustness of the determination of alternans data when using spectral domain methods.

Abstract

Method and apparatus for determining alternans data of an ECG signal. The method can include determining at least one value representing at least one morphology feature of each beat of the ECG signal and generating a set of data points based on a total quantity of values and a total quantity of beats. The data points can each include a first value determined using a first mathematical function and a second value determined using a second mathematical function. The method can also include several preprocessing algorithms to improve the signal to noise ratio. The method can also include separating the data points into a first group of points and a second group of points and generating a feature map by plotting the first group of points and the second group of points in order to assess an alternans pattern of variation. The feature map can be analyzed by statistical tests to determine the significance difference between groups and clusters.

Description

    BACKGROUND OF THE INVENTION
  • The present invention relates to cardiology, and more specifically to methods and apparatus for determining alternans data of an electrocardiogram (“ECG”) signal.
  • Alternans are a subtle beat-to-beat change in the repeating pattern of an ECG signal. Several studies have demonstrated a high correlation between an individual's susceptibility to ventricular arrhythmia and sudden cardiac death and the presence of a T-wave alternans (“TWA”) pattern of variation in the individual's ECG signal.
  • While an ECG signal typically has an amplitude measured in millivolts, an alternans pattern of variation with an amplitude on the order of a microvolt may be clinically significant. Accordingly, an alternans pattern of variation is typically too small to be detected by visual inspection of the ECG signal in its typical recorded resolution. Instead, digital signal processing and quantification of the alternans pattern of variation is necessary. Such signal processing and quantification of the alternans pattern of variation is complicated by the presence of noise and time shift of the alternans pattern of variation to the alignment points of each beat, which can be caused by limitation of alignment accuracy and/or physiological variations in the measured ECG signal. Current signal processing techniques utilized to detect TWA patterns of variation in an ECG signal include spectral domain methods and time domain methods.
  • BRIEF DESCRIPTION OF THE INVENTION
  • In light of the above, a need exists for a technique for detecting TWA patterns of variation in an ECG signal that provides improved performance as a stand-alone technique and as an add-on to other techniques. Accordingly, one or more embodiments of the invention provide methods and apparatus for determining alternans data of an ECG signal. In some embodiments, the method can include determining at least one value representing at least one morphology feature of each beat of the ECG signal and generating a set of data points based on a total quantity of values and a total quantity of beats. The data points can each include a first value determined using a first mathematical function and a second value determined using a second mathematical function. The method can also include separating the data points into a first group of points and a second group of points and generating a feature map by plotting the first group of points and the second group of points in order to assess an alternans pattern of variation.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a schematic diagram illustrating a cardiac monitoring system according to the invention.
  • FIG. 2 illustrates an ECG signal.
  • FIG. 3 is a flow chart illustrating one embodiment of a method of the invention.
  • FIG. 4 illustrates a maximum morphology feature.
  • FIG. 5 illustrates a minimum morphology feature.
  • FIG. 6 illustrates an area morphology feature.
  • FIG. 7 illustrates another area morphology feature.
  • FIG. 8 illustrates a further area morphology feature.
  • FIG. 9 illustrates still another area morphology feature.
  • FIG. 10 illustrates a plurality of beats, each beat being divided into a plurality of portions.
  • FIG. 11 illustrates a window establishing a size of one of the plurality of portions of FIG. 10.
  • FIG. 12 illustrates a feature matrix.
  • FIG. 13 illustrates a decomposition of the feature matrix of FIG. 12 as generated by a principal component analysis.
  • FIG. 14 illustrates a plot of values of data corresponding to values representative of a morphology feature.
  • FIG. 15 illustrates a determination of difference features using the values plotted in FIG. 14.
  • FIG. 16 illustrates another determination of difference features using the values plotted in FIG. 14.
  • FIG. 17 illustrates a further determination of a difference feature using the values plotted in FIG. 14.
  • FIG. 18 illustrates a feature map of first and second groups of points generated using values of a vector of data.
  • FIG. 19 illustrates a feature map generated using values of a vector of data generated by performing a principal component analysis on a feature matrix including the vector of data utilized to generate the feature map of FIG. 18.
  • FIG. 20 illustrates a feature map of first and second groups of points generated using a first mathematical function and a second mathematical function.
  • FIG. 21 illustrates a feature map of third and fourth groups of points generated using a third mathematical function and a fourth mathematical function.
  • FIG. 22 illustrates a feature map of fifth and sixth groups of points generated using a fifth mathematical function and the sixth mathematical function.
  • FIG. 23 illustrates a distance between a first center point of a first group of points and a second center point of a second group of points each plotted to form a feature map.
  • FIG. 24 illustrates a spectral graph generated using values of a vector of data.
  • FIG. 25 illustrates a spectral graph generated using values of a vector of data generated by performing a principal component analysis on a feature matrix including the vector of data utilized to generate the spectral graph of FIG. 24.
  • DETAILED DESCRIPTION
  • Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limited. The use of “including,” “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “mounted,” “connected” and “coupled” are used broadly and encompass both direct and indirect mounting, connecting and coupling. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings, and can include electrical connections or couplings, whether direct or indirect.
  • In addition, it should be understood that embodiments of the invention include both hardware and electronic components or modules that, for purposes of discussion, may be illustrated and described as if the majority of the components were implemented solely in hardware. However, one of ordinary skill in the art, and based on a reading of this detailed description, would recognize that, in at least one embodiment, the electronic based aspects of the invention may be implemented in software. As such, it should be noted that a plurality of hardware and software based devices, as well as a plurality of different structural components may be utilized to implement the invention. Furthermore, and as described in subsequent paragraphs, the specific mechanical configurations illustrated in the drawings are intended to exemplify embodiments of the invention and that other alternative mechanical configurations are possible.
  • FIG. 1 illustrates a cardiac monitoring system 10 according to some embodiments of the invention. The cardiac monitoring system 10 can acquire ECG data, can process the acquired ECG data to determine alternans data, and can output the alternans data to a suitable output device (e.g., a display, a printer, and the like). As used herein and in the appended claims, the term “alternans data” includes TWA data, or any other type of alternans data that is capable of being determined using one or more embodiments of the invention.
  • The cardiac monitoring system 10 can acquire ECG data using a data acquisition module. It should be understood that ECG data can be acquired from other sources (e.g., from storage in a memory device or a hospital information system). The data acquisition module can be coupled to a patient by an array of sensors or transducers which may include, for example, electrodes coupled to the patient for obtaining an ECG signal. In the illustrated embodiment, the electrodes can include a right arm electrode RA; a left arm electrode LA; chest electrodes V1, V2, V3, V4, V5 and V6; a right leg electrode RL; and a left electrode leg LL for acquiring a standard twelve-lead, ten-electrode ECG. In other embodiments, alternative configurations of sensors or transducers (e.g., less than ten electrodes) can be used to acquire a standard or non-standard ECG signal.
  • A representative ECG signal is schematically illustrated in FIG. 2. The ECG signal can include [G] beats including beat-one B1 through beat-[G] BG where [G] is a value greater than one. As used herein and in the appended claims, a capital letter in brackets represents a quantity, and a capital letter without brackets is a reference character (similar to a typical reference numeral).
  • The data acquisition module can include filtering and digitization components for producing digitized ECG data representing the ECG signal. In some embodiments, the ECG data can be filtered using low pass and baseline wander removal filters to remove high frequency noise and low frequency artifacts. The ECG data can, in some embodiments, be filtered by removing arrhythmic beats from the ECG data and by eliminating noisy beats from the ECG data.
  • The cardiac monitoring system 10 can include a processor and a memory associated with the processor. The processor can execute a software program stored in the memory to perform a method of the invention as illustrated in FIG. 3. FIG. 3 is a flow chart of a method of the invention used to determine and display alternans data of an ECG signal. Although the cardiac monitoring system 10 is described herein as including a single processor that executes a single software program, it should be understood that the system can include multiple processors, memories, and/or software programs. Further, the method of the invention illustrated in FIG. 3 can be performed manually or using other systems.
  • As shown in FIG. 3, the processor can receive (at 100) ECG data representing an ECG signal. The acquired ECG data can be received (e.g., from a patient in real-time via the data acquisition module or from storage in a memory device) and can be processed as necessary. The ECG data can represent continuous and/or non-continuous beats of the ECG signal. In one embodiment, the ECG data, or a portion thereof, can be parsed into a plurality of data sets. Each data set can represent a portion of a respective beat B of the ECG signal (e.g., the T-wave portion of a respective beat B of the ECG signal), a portion of a respective odd or even median beat of the ECG signal, a portion of a respective odd or even mean beat of the ECG signal, and the like. The parsed data sets can be saved in an array (e.g., a waveform array). In other embodiments, the ECG data can be saved in a single data set, or alternatively, saved in multiple data sets.
  • The processor can determine (at 102) a quantity [C] of values W representing a quantity [D] of morphology features F of a beat B (e.g., beat-one B1) of a quantity [G] beats, where [C] and [D] are each a quantity greater than or equal to one. In some embodiments, a single value W is determined for each morphology feature F (i.e., the quantity of [C] is equal to the quantity of [D]). However, in some embodiments, multiple values W are determined for a single morphology feature F and/or a single value W is determined for multiple morphology features F. Determining a quantity [C] of values W representing a quantity [D] of morphology features F can be repeated for a quantity [H−1] of beats of the quantity [G] of beats represented in the collected ECG data where a quantity [H] is greater than or equal to one and less than or equal to the quantity [G].
  • In some embodiments, any morphology features F of the beats B can be determined. FIGS. 4-9 illustrate some examples of such morphology features F. FIG. 4 illustrates a maximum morphology feature (i.e., the maximum value of the data set representing the T-wave portion of a respective beat). FIG. 5 illustrates a minimum morphology feature (i.e., the minimum value of the data set representing the T-wave portion of a respective beat). FIG. 6 illustrates an area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the minimum value of the data set). FIG. 7 illustrates another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the maximum value of the data set and a point of the data set representing the maximum up-slope of the curve). FIG. 8 illustrates still another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by the minimum value of the data set and a point of the data set representing the maximum down-slope of the curve). FIG. 9 illustrates yet another area morphology feature (i.e., the area between a curve formed by the data set representing the T-wave portion of a respective beat and a baseline established by a point of the data set representing the maximum up-slope of the curve and a point of the data set representing the maximum down-slope of the curve). Other types of maximum, minimum, and area morphology features can also be used.
  • Other examples of morphology features that can be used include amplitude morphology features (e.g., an amplitude of a point representing the maximum down-slope of the curve formed by the data set representing the T-wave portion of a respective beat) and slope morphology features (e.g., a maximum positive slope of the curve formed by the data set representing the T-wave portion of a respective beat). Another example is mathematical model morphology features obtained by determining values representing a mathematical model of the curve formed by the data set representing the T-wave portion of a respective beat using, for example, a Gaussian function model, a power of Cosine function model, and/or a bell function model. A further example is time interval morphology features (e.g., a time interval between a maximum value and a minimum value of the data set representing a T-wave portion of a respective beat). Still another example is shape correlation morphology features obtained by determining a value representing a shape correlation of the curve formed by the data set representing the T-wave portion of a respective beat using, for example, a cross-correlation method and/or an absolute difference correlation method. An additional example is ratio morphology features (e.g., a ST:T ratio). Any other suitable morphology feature can be used in other embodiments of the invention. In some embodiments, as discussed above, the morphology feature can be determined using values of the data set(s) of the ECG data. In other embodiments, the morphology features can be determined using values representing the values of the data set(s) of the ECG data (e.g., a morphology feature of the first derivative of the curve formed by a respective data set).
  • Morphology features can be determined using an entire parsed data set as illustrated in FIGS. 4-9, or alternatively, using a portion thereof as illustrated in FIGS. 10 and 11. As shown in FIG. 10, each of the beats B can be divided up in a plurality of portions. The center of each portion can be defined by a vertical divider line. As shown in FIG. 11, a window can be established to define the size of the portion. The window can include a single value of the data set (e.g., a value representing the point where the divider line crosses the curve formed by the data set), or values of the data set representing any number of points adjacent the intersection of the curve and the divider line.
  • As shown in FIG. 3, the processor can generate (at 104) a feature matrix. As used herein and in the appended claims, the term “matrix” includes any table of values. The generated feature matrix can include a quantity [C] of values W representing each of the quantity [D] of morphology features F for each of the quantity [H] of beats B (i.e., the feature matrix includes a quantity [C]×[H] of values W). Each value W can directly represent the determined morphology feature F (e.g., the actual value of the determined area morphology feature), or can indirectly represent the determined morphology feature (e.g., a normalized value of the determined area morphology feature).
  • A representative column-wise feature matrix A is illustrated in FIG. 12. The feature matrix A can include [C] columns and [H] rows. The feature matrix A can use the columns to represent the quantity [D] of morphology features F (i.e., each column includes a quantity [H] of values W of the same morphology feature as determined for each of the quantity [H] of beats B), and the rows to represent the beats B (i.e., each row includes a quantity [C] of values representing the quantity [D] of morphology features for each of the quantity [H] of beats). The values W of the morphology features F can be represented in the illustrated feature matrix A using the notation WIBJ and FIBJ where I is a value between one and [C], the quantity of [C] being equal to the quantity of [D], and J is a value between one and [H]. In other embodiments, the feature matrix A can be arranged in other suitable manners. In yet other embodiments, the values W representing the morphology features F can be saved for later processing.
  • As shown in FIG. 3, the processor can preprocess (at 106) the feature matrix A. In some embodiments, a principal component analysis (PCA) can be performed on the feature matrix A. PCA involves a multivariate mathematical procedure known as an eigen analysis which rotates the data to maximize the explained variance of the feature matrix A. In other words, a set of correlated variables are transformed into a set of uncorrelated variables which are ordered by reducing variability, the uncorrelated variables being linear combinations of the original variables. PCA is used to decompose the feature matrix A into three matrices, as illustrated in FIG. 13. The three matrices can include a matrix U, a matrix S, and a matrix V.
  • The matrix U can include the principal component vectors (e.g., the first principal component vector u1, the second principal component vector u2, . . . , the pth principal component vector up). The principal component vectors are also known as eigen vectors. The first principal component vector u1 can represent the most dominant variance vector (i.e., the first principal component vector u1 represents the largest beat-to-beat variance), the second principal component vector u2 can represent the second most dominant variance vector, and so on.
  • The S Matrix can include the principal components (e.g., the first principal component S1, the second principal component S2, . . . , the pth principal component Sp). The first principal component S1 can account for as much of the variability in the data as possible, and each succeeding principal component S can account for as much of the remaining variability as possible. The first principal component S1 can be used to determine alternans data (e.g., the square-root of the first PCA component S1 can provide an estimation of the amplitude of the most dominant alternans pattern of variation). In some embodiments, the second principal component S2 and the third principal component S3 can also provide useful alternans data.
  • The matrix V is generally known as the parameter matrix. The matrix V can be raised to a power of T. In other embodiments, the preprocessing of the feature matrix A can include other types of mathematical analyses.
  • The robustness of the preprocessing of the feature matrix A can be enhanced by increasing the quantity of [H] as the quantity of [D] increases. In other words, an increase in the number of morphology features F represented in the feature matrix A generally requires a corresponding increase in the number of beats B for which the morphology features F are being determined; The correspondence between the quantities of [D] and [H] is often based on the dependency between each of the [D] morphology features F. In some embodiments, the quantity of [H] is greater than or equal to 32 and less than or equal to 128. In other embodiments, the quantity of [H] is less than 32 or greater than 128. In some embodiments, the value of [H] is adaptively changed in response to a corresponding change in the level of noise in the measured ECG signal.
  • As shown in FIG. 3, the processor can determine (at 108) [E] points L using data corresponding to at least some of the values W, [E] being a quantity greater than or equal to one. The data corresponding to the values W can include at least one value W, at least one value of a principal component vector (e.g., the first principal component vector u1), and/or at least one value of any other data that corresponds to the values W. Each point L can include a first value (e.g., one of an X-value and a Y-value) determined using a first mathematical function Feature(beat+[N]), and a second value (e.g., the other of the X-value and the Y-value) determined using a second mathematical function Feature(beat), [N] being a quantity greater than or equal to one. Each of the first and second values of the points L represents a feature of the data corresponding to the values W. In the illustrated embodiment, the feature is a difference feature Q (i.e., the difference in amplitude between two values of the data corresponding to the values W as specified by the respective mathematical function). In other embodiments, the first and second values of the points L can represent another difference features (e.g., an absolute difference feature, a normalized difference feature, a square-root difference feature, and the like), or any other mathematically-definable feature of the data corresponding to the values W. For example, the feature can include a value feature where the feature is equal to a specified value of the data corresponding to the determined values W.
  • Equations 1 and 2 shown below define an example of the mathematical functions Feature(beat+[N]) and Feature(beat), respectively. The first values of the points L determined using the mathematical function Feature(beat+[N]) can represent a difference feature QK+[N] and the second values of the points L determined using the mathematical function Feature(beat) can represent the difference feature QK, where K is a value equal to a beat (i.e., the beat for which the respective mathematical function is being used to determine either the first or second value of a point L).
    Feature(beat+[N])=W (beat+2[N]) −W (beat+[N]) =Q K+[N]  [e1]
    Feature(beat)=W (beat+[N]) −W (beat) =Q K  [e2]
  • Tables 1-3 shown below represent the determination of points L using the mathematical functions Feature(beat+[N]) and Feature(beat) as defined in Equations 1 and 2 for [N]=1, 2, and 3, respectively. Equations 3 and 4 shown below define the mathematical functions Feature(beat+[N]) and Feature(beat) for [N]=1.
    Feature(beat+1)=W (beat+2) −W (beat+1) =Q K+1  [e3]
    Feature(beat)=W (beat+1) −W (beat) =Q K  [e4]
    Equations 5 and 6 shown below define the mathematical functions Feature(beat+[N]) and Feature(beat) for [N]=2.
    Feature(beat+2)=W (beat+4) −W (beat+2) =Q K+2  [e5]
    Feature(beat)=W (beat+2) −W (beat) =Q K  [e6]
    Equations 7 and 8 shown below define the mathematical functions Feature(beat+[N]) and Feature(beat) for [N]=3.
    Feature(beat+3)=W (beat+6) −W (beat+3) =Q K+3  [e7]
    Feature(beat)=W (beat+3) −W (beat) =Q K  [e8]
  • As shown by Equations 3-8, the offset between the difference feature QK+[N] and the difference feature QK is dependent on the value of [N]. For [N]=1, the first value of the point L is determined by finding the difference between the value W of the second next beat BI+2 and the value W of the next beat BI+1, while the second value of the point L is determined by finding the difference between the value W of the next beat BI+1 and the value W of the current beat BI. For [N]=2, the first value of the point L is determined by finding the difference between the value W of the fourth next beat BI+4 and the value W of the second next beat BI+2, while the second value of the point L is determined by finding the difference between the value W of the second next beat BI+2 and the value W of the current beat BI. For [N]=3, the first value of the point L is determined by finding the difference between the value W of the sixth next beat BI+6 and the value W of the third next beat BI+3, while the second value of the point L is determined by finding the difference between the value W of the third next beat BI+3 and the value W of the current beat BI. Accordingly, the first values of the points L determined using the first mathematical function Feature(beat+[N]) are offset relative to the second values of the points L determined using the second mathematical function Feature(beat) by a factor of [N]. For example, for [N]=1, the first mathematical function Feature(beat+[N]) determines Feature(2) . . . Feature(Z+1) for beat-one B1 through beat-(Z) BZ, while the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B1 through beat-(Z) BZ; for [N]=2, the first mathematical function Feature(beat+[N]) determines Feature(3) . . . Feature(Z+2) for beat-one B1 through beat-(Z) BZ, while the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B1 through beat-(Z) BZ; for [N]=3, the first mathematical function Feature(beat+[N]) determines Feature(4) . . . Feature(Z+3) for beat-one B1 through beat-(Z) BZ while the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B1 through beat-(Z) BZ. This offset relationship between the first values of the points L determined using the first mathematical function Feature(beat+[N]) and the second values of the points L determined using the second mathematical function Feature(beat) is further illustrated in Tables 1-3.
  • In Tables 1-3 shown below, the “Beat” column can represent respective beats B of the ECG signal and the “Feature Value” column can represent a value W of a morphology feature F of the corresponding respective beat B (e.g., an area morphology feature). As discussed above, the points L can be generated using values of other data corresponding to the determined values W. Also in Tables 1-3, an asterisk (*) represents an undetermined value of the point L (i.e., a value of the point L for which feature values W corresponding to beats B subsequent to the listed beats B1-B12 are required to determine the value of the point L), “f(b+N)” represents the mathematical function Feature(beat+[N]), and “f(b)” represent the mathematical function Feature(beat). Each point L shown in Tables 1-3 includes an X-value dtermined using the first mathematical function Feature(beat+[N]) and a Y-value determined using the second mathematical function Feature(beat).
    [N] = 1
    Feature f(b + N) = W(b+2N) − W(b+N) f(b) = W(b+N) − W(b) Feature Map
    Beat Value f(b + 1) = W(b+2) − W(b+1) f(b) = W(b+1) − W(b) Point Group
    1 2 f(2) = 3 − 5 = −2 f(1) = 5 − 2 = 3 (−2, 3) A
    2 5 f(3) = 6 − 3 = 3 f(2) = 3 − 5 = −2 (3, −2) B
    3 3 f(4) = 2 − 6 = −4 f(3) = 6 − 3 = 3 (−4, 3) A
    4 6 f(5) = 4 − 2 = 2 f(4) = 2 − 6 = −4 (2, −4) B
    5 2 f(6) = 3 − 4 = −1 f(5) = 4 − 2 = 2 (−1, 2) A
    6 4 f(7) = 7 − 3 = 4 f(6) = 3 − 4 = −1 (4, −1) B
    7 3 f(8) = 3 − 7 = −4 f(7) = 7 − 3 = 4 (−4, 4) A
    8 7 f(9) = 5 − 3 = 2 f(8) = 3 − 7 = −4 (2, −4) B
    9 3 f(10) = 3 − 5 = −2 f(9) = 5 − 3 = 2 (−2, 2) A
    10 5 f(11) = 7 − 3 = 4 f(10) = 3 − 5 = −2 (4, −2) B
    11 3 f(12) = W13 − 7 = * f(11) = 7 − 3 = 4 (*, 4) A
    12 7 f(13) = W14 − W13 = * f(12) = W13 − 7 = * (*, *) B
  • [N] = 2
    Feature f(b + N) = W(b+2N) − W(b+N) f(b) = W(b+N) − W(b) Feature Map
    Beat Value f(b + 2) = W(b+4) − W(b+2) f(b) = W(b+2) − W(b) Point Group
    1 2 f(3) = 2 − 3 = −1 f(1) = 3 − 2 = 1 (−1, 1) A
    2 5 f(4) = 4 − 6 = −2 f(2) = 6 − 5 = 1 (−2, 1) B
    3 3 f(5) = 3 − 2 = 1 f(3) = 2 − 3 = −1 (1, −1) A
    4 6 f(6) = 7 − 4 = 3 f(4) = 4 − 6 = −2 (3, −2) B
    5 2 f(7) = 3 − 3 = 0 f(5) = 3 − 2 = 1 (0, 1) A
    6 4 f(8) = 5 − 7 = −2 f(6) = 7 − 4 = 3 (−2, 3) B
    7 3 f(9) = 3 − 3 = 0 f(7) = 3 − 3 = 0 (0, 0) A
    8 7 f(10) = 7 − 5 = 2 f(8) = 5 − 7 = −2 (2, −2) B
    9 3 f(11) = W13 − 3 = * f(9) = 3 − 3 = 0 (*, *) A
    10 5 f(12) = W14 − 7 = * f(10) = 7 − 5 = 2 (*, *) B
    11 3 f(13) = W15 − W13 = * f(11) = W13 − 3 = * (*, *) A
    12 7 f(14) = W16 − W14 = * f(12) = W14 − 7 = * (*, *) B
  • [N] = 3
    Feature f(b+N) = W(b+2N) − W(b+N) f(b) = W(b+N) − W(b) Feature Map
    Beat Value f(b+3) = W(b+6) − W(b+3) f(b) = W(b+3) − W(b) Point Group
    1 2 f(4) = 3 − 6 = −3 f(1) = 6 − 2 = 4 (−3, 4) A
    2 5 f(5) = 7 − 2 = 5 f(2) = 2 − 5 = −3 (5, −3) B
    3 3 f(6) = 3 − 4 = −1 f(3) = 4 − 3 = 1 (−1, 1) A
    4 6 f(7) = 5 − 3 = 2 f(4) = 3 − 6 = −3 (2, −3) B
    5 2 f(8) = 3 − 7 = −4 f(5) = 7 − 2 = 5 (−4, 5) A
    6 4 f(9) = 7 − 3 = 4 f(6) = 3 − 4 = −1 (4, −1) B
    7 3 f(10) = W13 − 5 = * f(7) = 5 − 3 = 2 (*, *) A
    8 7 f(11) = W14 − 3 = * f(8) = 3 − 7 = −4 (*, *) B
    9 3 f(12) = W15 − 7 = * f(9) = 7 − 3 = 4 (*, *) A
    10 5 f(13) = W16 − W13 = * f(10) = W13 − 5 = * (*, *) B
    11 3 f(14) = W17 − W14 = * f(11) = W14 − 3 = * (*, *) A
    12 7 f(15) = W18 − W15 = * f(12) = W15 − 7 = * (*, *) B
  • FIG. 14 illustrates a plot of the feature values from Tables 1-3 for beat-one B1 through beat-seven B7 where each peak and each valley of the plot can represent a respective feature value W (e.g., value-one W1 which represents beat-one B1, value-two W2 which represents beat-two B2, . . . , value-seven W7 which represents beat-seven B7).
  • FIG. 15 illustrates for [N]=1 how the mathematical functions Feature(beat+[N]) and Feature(beat) determine the first and second values of the points L which represent the difference features QK and QK+1. For [N]=1, the seven values (i.e., value-one W1 through value-seven W7) generate six difference features (i.e., difference feature-one Q1 through difference feature-six Q6). Referring to Table 1, the first mathematical function generates difference feature-two Q2 through difference feature-six Q6 for beat-one B1 through beat-five B5, respectively, using the seven values, and the second mathematical function generates difference feature-one Q1 through difference feature-six Q6 for beat-one B1 through beat-six B6, respectively, using the seven values.
  • The difference feature Q is illustrated in FIG. 15 as dotted-line arrows extending between two specified values of the plot of FIG. 14. As an example, to determine difference feature-three Q3 (i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-two B2, the second value of the point L as determined by the second mathematical function Feature(beat) for beat-three B3), the difference can be found between value-four W4 which represents beat-four B4 and value-three W3 which represents beat-three B3. Similarly, to determine difference feature-six Q6 (i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-two B5, the second value of the point L as determined by the second mathematical function Feature(beat) for beat-six B6), the difference can be found between value-four W7 which represents beat-seven B7 and value-six W6 which represents beat-six B6.
  • FIG. 16 illustrates for [N]=2 how the mathematical functions Feature(beat+[N]) and Feature(beat) determine the first and second values of the points L which represent the difference features QK and QK+2. For [N]=2, the seven values (i.e., value-one W1 through value-seven W7) generate five difference features (i.e., difference feature-one Q1 through difference feature-five Q5). Referring to Table 2, the first mathematical function generates difference feature-three Q3 through difference feature-five Q5 for beat-one B1 through beat-three B3, respectively, using the seven values, and the second mathematical function generates difference feature-one Q1 through difference feature-five Q5 for beat-one B1 through beat-five B5, respectively, using the seven values.
  • The difference feature Q is illustrated in FIG. 16 as dotted-line arrows extending between two specified values of the plot of FIG. 14. As an example, to determine difference feature-three Q3 (i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-one B1, the second value of the point L as determined by the second mathematical function Feature(beat) for beat-three B3), the difference can be found between value-five W5 which represents beat-five B5 and value-three W3 which represents beat-three B3. Similarly, to determine difference feature-five Q5 (i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-three B3, the second value of the point L as determined by the second mathematical function Feature(beat) for beat-five B5), the difference can be found between value-four W7 which represents beat-seven B7 and value-five W5 which represents beat-five B5.
  • FIG. 17 illustrates for [N]=3 how the mathematical functions Feature(beat+[N]) and Feature(beat) determine the first and second values of the points L which represent the difference features QK and QK+3. For [N]=3, the seven values (i.e., value-one W1 through value-seven W7) generate four difference features (i.e., difference feature-one Q1 through difference feature-four Q4). Referring to Table 3, the first mathematical function generates difference feature-four Q4 for beat-four B4 using the seven values, and the second mathematical function generates difference feature-one Q1 through difference feature-four Q4 for beat-one B1 through beat-four B4, respectively, using the seven values.
  • The difference feature Q is illustrated in FIG. 17 as dotted-line arrows extending between two specified values of the plot of FIG. 14. As an example, to determine difference feature-three Q4 (i.e., the first value of the point L as determined by the first mathematical function Feature(beat+[N]) for beat-one B1, the second value of the point L as determined by the second mathematical function Feature(beat) for beat-three B3), the difference can be found between value-seven W7 which represents beat-seven B7 and value-four W4 which represents beat-four B4.
  • As shown by the “Group” column of Tables 1-3, each point L can be assigned to a respective group (e.g., group A or group B). The points L representing each odd beat (e.g., beat-one B1, beat-three B3, . . . , beat-eleven B11) can be assigned to a first group (i.e., group A), and the points representing each even beat (e.g., beat-two B2, beat-four B4, . . . , beat-twelve B12) can be assigned to a second group (i.e., group B). The points L can be assigned to group A and group B in this manner to represent a proposed odd-even alternans pattern of variation (i.e., ABAB . . . ). In other embodiments, the points L can be alternatively assigned to groups to represent other proposed alternans patterns of variation (e.g., AABBAABB . . . , AABAAB . . . , and the like).
  • As shown in FIG. 3, the processor can plot (at 110) a feature map [e.g., a feature map of Feature(beat+[N]) versus Feature(beat)]. Both groups of points L (e.g., group A and group B) can be plotted on the same axis to generate the feature map. The polarity of the differences of the group A points are inverted relative to the polarities of the differences of the group B points. As a result, plotting the points L determined using the mathematical functions Feature(beat) and Feature(beat+[N]) as defined by Equations 1 and 2 can accentuate any difference between the values specified by the mathematical functions Feature(beat) and Feature(beat+[N]). The inverted polarity of the differences between the first and second groups is illustrated in FIGS. 15-17 where the direction of the dotted-line arrows that represent the difference features Q alternates between adjacent difference features Q.
  • The feature map provides a visual indication of the divergence of the two groups of points, and thus the existence of a significant alternans pattern of variation. If there is a significant ABAB . . . alternans pattern of variation, the two groups of points will show separate clusters on the feature map (for example, as shown in FIGS. 20 and 22). If there is not a significant ABAB . . . alternans pattern of variation, the feature map will illustrate a more random pattern of points from the two groups (for example, as shown in FIG. 21).
  • FIGS. 18 and 19 illustrate two examples of feature maps. The [E] points plotted to generate the feature maps of FIGS. 18 and 19 were determined using ECG data representative of an ECG signal having a 5 microvolt TWA pattern of variation, 20 microvolts of noise, and 20 milliseconds of offset, where [H] is equal to 128. The first and second groups of points can be distinguished by the markers utilized to represent the points of the group (i.e., the first group of points, group A, can include asterisks shaped markers, and the second group of points, group B, can include round markers). Lines can be used to connect sequential markers of each group (e.g., for group A, point-two P2A can be connected to each of point-one P1A and point-three P3A by lines).
  • The feature map of FIG. 18 illustrates a plot of points determined using values directly from the feature matrix A (i.e., the feature matrix A was not preprocessed using a principal component analysis or other mathematical analysis). As illustrated in FIG. 18, the points of the first and second groups are intermixed (i.e., the feature map illustrates a random pattern of the points from the two groups). Accordingly, the feature map of FIG. 18 does not illustrate the presence of a significant divergence of the two groups of points, and thus, does not indicate the existence of a significant alternans pattern of variation.
  • The feature map of FIG. 19 illustrates a plot of points determined using values of a first principal vector u1. The first principal vector u1 is a result of a principal component analysis performed on the same feature matrix A from which the values used to determine the points L plotted in FIG. 18 were obtained. As illustrated in FIG. 19, although the first and second groups of points are partially overlapped, the first group of points is primarily positioned in the upper-left quadrant of the feature map and the second group of points is primarily positioned in the lower-right quadrant of the feature map. Accordingly, the feature map of FIG. 19 appears to illustrate the presence of a significant divergence of the two groups of points, and thus, a significant alternans pattern of variation may exist.
  • Although FIGS. 18 and 19 illustrate the same ECG data, the feature map of FIG. 19 indicates the existence of an alternans pattern of variation, while the feature map of FIG. 18 does not. The effect of noise and time shift in the measured ECG signal on the determined alternans data is clearly indicated by the feature maps of FIGS. 18 and 19. Preprocessing the feature matrix A increases the robustness of the determination of alternans data by limiting the effect of noise and time shift in the measured ECG signal.
  • In some embodiments, multiple feature maps can be generated for various quantities of [N] using the same set of values (e.g., the feature maps for [N]=1, 2, and 3, respectively, can be generated using the points determined in Tables 1-3). The display of multiple feature maps can further verify the existence of a significant alternans pattern of variation for the proposed alternans pattern of variation (e.g., a ABAB . . . alternans pattern of variation).
  • FIGS. 20-22 illustrate feature maps for [N]=1, 2, and 3, respectively, where the points plotted in each of the feature maps were determined using the same set of values. The divergence of the first and second groups of points in the feature maps of FIGS. 20 and 22 in combination with the lack of divergence of the first and second groups of points in the feature map of FIG. 21 provides visual evidence that the proposed ABAB . . . alternans pattern of variation is correct.
  • The operator can change the proposed alternans pattern of variation (i.e., change the grouping of the points to a different alternans pattern of variation) if the feature maps for [N]=1, 2, and 3 do illustrate differing divergence patterns for [N]=1 and 3 and [N]=2, respectively. For example, if the two groups of points diverge in the feature map for [N]=1 and 2, but not for the feature maps of [N]=3, the ECG signal represented by the values used to determine the points for the feature maps does not represent the proposed ABAB . . . alternans pattern of variation. However, the ECG signal can include a different alternans pattern of variation. Reassignment of the [E] points to different groups can be used to test a different proposed alternans pattern of variation.
  • As shown in FIG. 3, the processor (at 112) can statistically analyze the data plotted in the feature map. Although the feature map provides a visual indication of the existence of a significant alternans pattern of variation, the feature map does not provide a quantitative measure of the confidence level of the alternans pattern of variation. Accordingly, the data plotted in the feature map, or similar types of data that are not plotted in a feature map, can be statistically analyzed to provide such quantitative measures of the confidence level of the alternans pattern of variation.
  • In some embodiments, a paired T-test can be performed on the first and second groups of points. A paired T-test is a statistical test which is performed to determine if there is a statistically significant difference between two means. The paired T-test can provide a p-value (e.g., p=0.001). In one embodiment, the confidence level is increased (i.e., a significant alternans pattern of variation exists) when the p-value is less than 0.001. In other embodiments, other suitable threshold levels can be established.
  • In some embodiments, a cluster analysis (e.g., a fuzzy cluster analysis or a K-mean cluster analysis) can be performed on the [E] points to determine a first cluster of points and a second cluster of points. The cluster analysis can also generate a first center point for the first cluster and a second center point for the second cluster. The first and second clusters of points can be compared with the first and second groups of points, respectively. A determination can be made of the number of clustered points that match the corresponding grouped points. For example, if point-one L1 and point-two L2 are clustered in the first cluster, point-three L3 and point-four L4 are clustered in the second cluster, point-one L1, point-two L2, and point-three L3 can be grouped in the first group, and point-four L4 can be grouped in the second group. Clustered point-three L3 does not correspond to grouped point-three L3, thereby resulting in a 75% confidence level. The confidence level can represent the percentage of clustered points that match the corresponding grouped points. In one embodiment, a confidence level about 90% can be a high confidence level, a confidence level between 60% and 90% can be a medium confidence level, and a confidence level below 60% can be a low confidence level. In other embodiments, the thresholds for the high, medium, and/or low confidence levels can be other suitable ranges of percentages or values.
  • As shown in FIG. 3, the processor can determine (at 114) an estimate of an amplitude of the alternans pattern of variation. As discussed above, in one embodiment, the square-root of a principal component (e.g., the first principal component S1) can be used to provide an estimate of the amplitude. In other embodiments, a distance can be determined between a first center point of a first group of points and a second center point of a second group of points. The center points can include the center points of the first and second groups of points A and B as determined using a mathematical analysis (e.g., by taking the mean or median of the values of the points for each respective group), the center points provided by the Paired T-test, the center points provided by the cluster analysis, or any other determined center points that represent the ECG data.
  • FIG. 23 illustrates a distance measurement between the first and second center points. The distance can be determined using Equation 9 shown below, where the first center point includes an X-value X1 and a Y-value Y1 and the second center point includes an X-value X2 and a Y-value Y2.
    AmplitudeESTIMATE=√{square root over ((X 1 −X 2)2+(Y 1 −Y 2)2)}  [e9]
  • The amplitude of the alternans pattern of variation often depends on the [D] morphology features used to determine the values W. Accordingly, the estimated amplitude is generally not an absolute value that can be compared against standardized charts. However, comparisons can be generated for estimated amplitudes of alternans patterns of variation based on the morphology features F that are determined and the processing step that is used.
  • As shown in FIG. 3, the processor can report (at 116) alternans data to a caregiver and/or the processor can store the alternans data. The alternans data (e.g., the feature maps, the estimated amplitudes of the alternans pattern of variation, the confidence level of the alternans pattern of variation, the uncertainty level of the alternans pattern of variation, the p-value of the alternans pattern of variation, and the like) can be reported using any suitable means (e.g., output to a suitable output device such as a display, a printer, and the like).
  • As shown in FIG. 3, in some embodiments, the processor can plot (at 118) a spectral graph using values resulting from preprocessing the feature matrix (e.g., the values of the first principal component vector u1). FIGS. 24 and 25 illustrate two examples of spectral graphs. The values used to generate the spectral graphs of both FIGS. 24 and 25 were determined using ECG data representative of an ECG signal having a 5 microvolt TWA pattern of variation, 20 microvolts of noise, and 20 milliseconds of offset, where [H] is equal to 128.
  • FIG. 24 illustrates a spectral graph generated using values directly from the feature matrix A (i.e., the feature matrix A was not preprocessed using a principal component analysis or other mathematical analysis). As illustrated in FIG. 24, the spectral graph does not include a dominant frequency at half of the beat sample frequency, but instead includes a number of frequency spikes having varying amplitudes. Accordingly, the spectral graph of FIG. 24 does not indicate the existence of a significant alternans pattern of variation. FIG. 25 illustrates a spectral graph generated using values of a first principal vector u1. The first principal vector u1 is a result of a principal component analysis performed on the same feature matrix A from which the values used to generate the spectral graph of FIG. 24 were obtained. FIG. 25 illustrates a single frequency spike at half of the beat sample frequency. Accordingly, unlike the spectral graph of FIG. 24, the spectral graph of FIG. 25 appears to illustrate the presence of a significant alternans pattern of variation. The effect of noise and time shift in the measured ECG signal on the determined alternans data is indicated by the spectral graphs of FIGS. 24 and 25. Preprocessing the feature matrix A increases the robustness of the determination of alternans data when using spectral domain methods.

Claims (20)

1-15. (canceled)
16. A method of determining alternans data of an ECG signal, the method comprising:
determining at least one value representing at least one morphology feature of each beat of the ECG signal;
generating a feature matrix based on a total quantity of values and a total quantity of beats;
processing the feature matrix using a principal component analysis, the principal component analysis generating principal component vectors and principal components; and
using data corresponding to at least one of the principal component vectors and the principal components to determine the alternans data.
17. A method as set forth in claim 16 and further comprising generating a feature map based on at least one of the principal component vectors.
18. A method as set forth in claim 16 and further comprising generating a spectral graph based on at least one of the principal component vectors.
19-23. (canceled)
24. The method as set forth in claim 16 and further comprising
determining an estimated amplitude of the alternans pattern of variation by calculating a square root of at least one of the principal components.
25. A method as set forth in claim 16 wherein the principal component analysis includes performing an Eigen analysis.
26. A method as set forth in claim 16 and further comprising the principal component analysis decomposing the feature matrix into a matrix U, a matrix S, and a matrix B.
27. A method as set forth in claim 26 wherein the matrix U includes the principal component vectors.
28. A method as set forth in claim 26 wherein the matrix S includes the principal components.
29. A method as set forth in claim 26 wherein the matrix B is a perimeter matrix.
30. A device for determining alternans data of a ECG signal, the device comprising:
means for determining at least one value representing at least one morphology feature of each beat of the ECG signal;
means for generating a feature matrix based on a total quantity of values and a total quantity of beats;
means for processing the feature matrix using a principal component analysis, the principal component analysis generating principal component vectors and principal components; and
means for using data corresponding to at least one of the principal component vectors and the principal components to determine the alternans data.
31. A device as set forth in claim 30 and further comprising means for generating a feature map based on at least one of the principal component vectors.
32. A device as set forth in claim 30 and further comprsing means for generating a spectral graph based on at least one of the principal component vectors.
33. A device as set forth in claim 30 and further comprising means for determining an estimated amplitude of the alternans pattern of variation by calculating a square root of at least one of the principal components.
34. A device as set forth in claim 30 wherein the principal component analysis includes performing an Eigen analysis.
35. A device as set forth in claim 30 and further comprising the principal component analysis decomposing the feature matrix into a matrix U, a matrix S, and a matrix B.
36. A device as set forth in claim 35 wherein the matrix U includes the principal component vectors.
37. A device as set forth in claim 35 wherein the matrix S includes the principal components.
38. A device as set forth in claim 35 wherein the matrix B is a perimeter matrix.
US11/393,575 2004-04-15 2006-03-30 Method and apparatus for determining alternans data of an ECG signal Abandoned US20060173371A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US11/393,575 US20060173371A1 (en) 2004-04-15 2006-03-30 Method and apparatus for determining alternans data of an ECG signal

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/825,495 US7072709B2 (en) 2004-04-15 2004-04-15 Method and apparatus for determining alternans data of an ECG signal
US11/393,575 US20060173371A1 (en) 2004-04-15 2006-03-30 Method and apparatus for determining alternans data of an ECG signal

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US10/825,495 Division US7072709B2 (en) 2004-04-15 2004-04-15 Method and apparatus for determining alternans data of an ECG signal

Publications (1)

Publication Number Publication Date
US20060173371A1 true US20060173371A1 (en) 2006-08-03

Family

ID=35097198

Family Applications (3)

Application Number Title Priority Date Filing Date
US10/825,495 Active 2024-06-29 US7072709B2 (en) 2004-04-15 2004-04-15 Method and apparatus for determining alternans data of an ECG signal
US11/393,575 Abandoned US20060173371A1 (en) 2004-04-15 2006-03-30 Method and apparatus for determining alternans data of an ECG signal
US11/393,614 Active 2028-02-17 US8068900B2 (en) 2004-04-15 2006-03-30 Method and apparatus for determining alternans data of an ECG signal

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US10/825,495 Active 2024-06-29 US7072709B2 (en) 2004-04-15 2004-04-15 Method and apparatus for determining alternans data of an ECG signal

Family Applications After (1)

Application Number Title Priority Date Filing Date
US11/393,614 Active 2028-02-17 US8068900B2 (en) 2004-04-15 2006-03-30 Method and apparatus for determining alternans data of an ECG signal

Country Status (2)

Country Link
US (3) US7072709B2 (en)
CN (1) CN1682654A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102512157A (en) * 2011-12-15 2012-06-27 重庆大学 Dynamic electrocardiogram T wave alternate quantitative analysis method based on models
CN103431857A (en) * 2013-09-09 2013-12-11 苏州百慧华业精密仪器有限公司 Method for automatically scanning suspicious T wave alteration (TWA) positive sections of Holter
US9149201B2 (en) 2012-03-30 2015-10-06 Nihon Kohden Corporation TWA measuring apparatus and TWA measuring method

Families Citing this family (49)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100010333A1 (en) * 2005-07-29 2010-01-14 Jorge Hernando Ordonez-Smith Bipolar, Non-Vectorial Electrocardiography
US7072709B2 (en) * 2004-04-15 2006-07-04 Ge Medical Information Technologies, Inc. Method and apparatus for determining alternans data of an ECG signal
US9015263B2 (en) 2004-10-29 2015-04-21 Go Daddy Operating Company, LLC Domain name searching with reputation rating
DE102005014761A1 (en) * 2005-03-31 2006-10-05 Siemens Ag Method for arranging object data in electronic cards
EP1981402B1 (en) * 2006-02-06 2016-08-10 The Board Of Trustees Of The Leland Stanford Junior University Non-invasive cardiac monitor
US7840259B2 (en) * 2006-11-30 2010-11-23 General Electric Company Method and system for electrocardiogram evaluation
US7769434B2 (en) * 2006-11-30 2010-08-03 General Electric Company Method of physiological data analysis and measurement quality check using principal component analysis
GB0624081D0 (en) * 2006-12-01 2007-01-10 Oxford Biosignals Ltd Biomedical signal analysis method
GB0624085D0 (en) 2006-12-01 2007-01-10 Oxford Biosignals Ltd Biomedical signal analysis method
US8407173B2 (en) * 2008-01-30 2013-03-26 Aptima, Inc. System and method for comparing system features
US20090248736A1 (en) * 2008-03-26 2009-10-01 The Go Daddy Group, Inc. Displaying concept-based targeted advertising
US8069187B2 (en) * 2008-03-26 2011-11-29 The Go Daddy Group, Inc. Suggesting concept-based top-level domain names
US7962438B2 (en) * 2008-03-26 2011-06-14 The Go Daddy Group, Inc. Suggesting concept-based domain names
US7904445B2 (en) * 2008-03-26 2011-03-08 The Go Daddy Group, Inc. Displaying concept-based search results
US20090313363A1 (en) * 2008-06-17 2009-12-17 The Go Daddy Group, Inc. Hosting a remote computer in a hosting data center
WO2010000009A1 (en) * 2008-07-02 2010-01-07 Cardanal Pty Ltd Improved detection of cardiac dysfunction
CN101427917B (en) * 2008-09-08 2011-02-09 电子科技大学 ECG abnormal acquiring method based on inherent trend subsequence mode decomposition
US8019407B2 (en) * 2008-10-24 2011-09-13 Biotronik Crm Patent Ag Heart monitoring device and method
US20100145205A1 (en) * 2008-12-05 2010-06-10 Cambridge Heart, Inc. Analyzing alternans from measurements of an ambulatory electrocardiography device
ES2343054B2 (en) * 2009-01-20 2011-02-07 Universidad De Alcala DEVICE AND METHOD FOR THE DETECTION OF THE ALTERNANCE OF VENTRICULAR REPOLARIZATION THROUGH WINDING.
KR101513288B1 (en) 2010-05-12 2015-04-17 아이리듬 테크놀로지스, 아이엔씨 Device features and design elements for long-term adhesion
CN102485172B (en) * 2010-12-01 2015-02-25 通用电气公司 Detection method and system for detecting peak point of T waves
US9002926B2 (en) 2011-04-22 2015-04-07 Go Daddy Operating Company, LLC Methods for suggesting domain names from a geographic location data
CN103890780B (en) * 2011-09-29 2018-01-02 皇家飞利浦有限公司 The signal detection that distortion reduces
US8489182B2 (en) 2011-10-18 2013-07-16 General Electric Company System and method of quality analysis in acquisition of ambulatory electrocardiography device data
JP5926074B2 (en) * 2012-03-06 2016-05-25 大名 魏 TWA measuring device and method of operating TWA measuring device
JP6002584B2 (en) 2013-01-10 2016-10-05 日本光電工業株式会社 TWA measuring device
WO2014116825A1 (en) 2013-01-24 2014-07-31 Irhythm Technologies, Inc. Physiological monitoring device
WO2014168841A1 (en) 2013-04-08 2014-10-16 Irhythm Technologies, Inc Skin abrader
WO2014199291A1 (en) 2013-06-11 2014-12-18 Koninklijke Philips N.V. Synchronized cardioversion mixed mode operation and timing verification
US11147499B2 (en) 2013-08-30 2021-10-19 Joseph Wiesel Method and apparatus for detecting atrial fibrillation
US9681819B2 (en) * 2013-08-30 2017-06-20 Joseph Wiesel Method and apparatus for detecting atrial fibrillation
US10905335B2 (en) 2013-09-25 2021-02-02 Zoll Medical Corporation Emergency medical services smart watch
US10092236B2 (en) 2013-09-25 2018-10-09 Zoll Medical Corporation Emergency medical services smart watch
US9715694B2 (en) 2013-10-10 2017-07-25 Go Daddy Operating Company, LLC System and method for website personalization from survey data
US9684918B2 (en) 2013-10-10 2017-06-20 Go Daddy Operating Company, LLC System and method for candidate domain name generation
US11357413B2 (en) * 2014-03-06 2022-06-14 Healthy.Io Ltd. Methods and apparatus for self-calibrating non-invasive cuffless blood pressure measurements
US9668665B2 (en) * 2014-08-13 2017-06-06 Cameron Health, Inc. Methods and implantable devices for detecting arrhythmia
US9953105B1 (en) 2014-10-01 2018-04-24 Go Daddy Operating Company, LLC System and method for creating subdomains or directories for a domain name
EP4218580A1 (en) 2014-10-31 2023-08-02 Irhythm Technologies, Inc. Wireless physiological monitoring device and systems
US9785663B2 (en) 2014-11-14 2017-10-10 Go Daddy Operating Company, LLC Verifying a correspondence address for a registrant
US9779125B2 (en) 2014-11-14 2017-10-03 Go Daddy Operating Company, LLC Ensuring accurate domain name contact information
CN107530015B (en) * 2015-04-20 2021-12-14 深圳市长桑技术有限公司 Vital sign analysis method and system
US10123195B1 (en) * 2018-03-22 2018-11-06 Mapsted Corp. Method and system of crowd- sourced pedestrian localization
US10686715B2 (en) 2018-05-09 2020-06-16 Biosig Technologies, Inc. Apparatus and methods for removing a large-signal voltage offset from a biomedical signal
CN111067508B (en) * 2019-12-31 2022-09-27 深圳安视睿信息技术股份有限公司 Non-intervention monitoring and evaluating method for hypertension in non-clinical environment
US11083371B1 (en) 2020-02-12 2021-08-10 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network
US11246523B1 (en) 2020-08-06 2022-02-15 Irhythm Technologies, Inc. Wearable device with conductive traces and insulator
KR20230047455A (en) 2020-08-06 2023-04-07 아이리듬 테크놀로지스, 아이엔씨 Adhesive Physiological Monitoring Device

Citations (83)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3544187A (en) * 1968-10-23 1970-12-01 Sperry Rand Corp Filing cabinet
US3658055A (en) * 1968-05-20 1972-04-25 Hitachi Ltd Automatic arrhythmia diagnosing system
US3759248A (en) * 1971-02-08 1973-09-18 Spacelabs Inc Cardiac arrythmia detector
US3821948A (en) * 1971-11-03 1974-07-02 Hoffmann La Roche System and method for analyzing absolute derivative signal from heartbeat
US3902479A (en) * 1973-02-16 1975-09-02 Hoffmann La Roche Method and apparatus for heartbeat rate monitoring
US3952731A (en) * 1973-12-15 1976-04-27 Ferranti Limited Cardiac monitoring apparatus
US4124894A (en) * 1974-10-15 1978-11-07 Hycel, Inc. Apparatus and method for reporting detected error in a cardiac signal
US4136690A (en) * 1977-10-31 1979-01-30 Del Mar Avionics Method and apparatus for vector analysis of ECG arrhythmias
US4170992A (en) * 1978-01-05 1979-10-16 Hewlett-Packard Company Fiducial point location
US4181135A (en) * 1978-03-03 1980-01-01 American Optical Corporation Method and apparatus for monitoring electrocardiographic waveforms
US4202340A (en) * 1975-09-30 1980-05-13 Mieczyslaw Mirowski Method and apparatus for monitoring heart activity, detecting abnormalities, and cardioverting a malfunctioning heart
US4316249A (en) * 1979-09-28 1982-02-16 Hittman Corporation Automatic high speed Holter scanning system
US4417306A (en) * 1980-01-23 1983-11-22 Medtronic, Inc. Apparatus for monitoring and storing utilizing a data processor
US4422459A (en) * 1980-11-18 1983-12-27 University Patents, Inc. Electrocardiographic means and method for detecting potential ventricular tachycardia
US4432375A (en) * 1982-05-24 1984-02-21 Cardiac Resuscitator Corporation Cardiac arrhythmia analysis system
US4457315A (en) * 1978-09-18 1984-07-03 Arvin Bennish Cardiac arrhythmia detection and recording
US4458691A (en) * 1982-02-11 1984-07-10 Arrhythmia Research Technology, Inc. System and method for predicting ventricular tachycardia by adaptive high pass filter
US4458692A (en) * 1982-02-11 1984-07-10 Arrhythmia Research Technology, Inc. System and method for predicting ventricular tachycardia with a gain controlled high pass filter
US4475558A (en) * 1982-05-28 1984-10-09 Healthdyne, Inc. System for providing short-term event data and long-term trend data
US4492235A (en) * 1982-02-11 1985-01-08 Arrhythmia Research Technology, Inc. System and method for predicting ventricular tachycardia by derivative analysis
US4519395A (en) * 1982-12-15 1985-05-28 Hrushesky William J M Medical instrument for noninvasive measurement of cardiovascular characteristics
US4583553A (en) * 1983-11-15 1986-04-22 Medicomp, Inc. Ambulatory ECG analyzer and recorder
US4589420A (en) * 1984-07-13 1986-05-20 Spacelabs Inc. Method and apparatus for ECG rhythm analysis
US4603703A (en) * 1984-04-13 1986-08-05 The Board Of Trustees Of The Leland Stanford Junior University Method for real-time detection and identification of neuroelectric signals
US4616659A (en) * 1985-05-06 1986-10-14 At&T Bell Laboratories Heart rate detection utilizing autoregressive analysis
US4665485A (en) * 1983-07-22 1987-05-12 Lundy Research Laboratories, Inc. Method and apparatus for characterizing the unknown state of a physical system
US4679144A (en) * 1984-08-21 1987-07-07 Q-Med, Inc. Cardiac signal real time monitor and method of analysis
US4680708A (en) * 1984-03-20 1987-07-14 Washington University Method and apparatus for analyzing electrocardiographic signals
US4732157A (en) * 1986-08-18 1988-03-22 Massachusetts Institute Of Technology Method and apparatus for quantifying beat-to-beat variability in physiologic waveforms
US4796638A (en) * 1984-09-28 1989-01-10 Kabushiki Kaisya Advance Kaihatsu Kenkyujo Artifact detecting apparatus in the measurement of a biological signal
US4802491A (en) * 1986-07-30 1989-02-07 Massachusetts Institute Of Technology Method and apparatus for assessing myocardial electrical stability
US4832038A (en) * 1985-06-05 1989-05-23 The Board Of Trustees Of University Of Illinois Apparatus for monitoring cardiovascular regulation using heart rate power spectral analysis
US4854327A (en) * 1988-03-07 1989-08-08 Kunig Horst E Non-invasive and continuous cardiac performance monitoring device
US4860762A (en) * 1988-06-03 1989-08-29 Hewlett-Packard Company Dual channel resolver for real time arrythmia analysis
US4896677A (en) * 1987-12-26 1990-01-30 Fukuda Denshi Kabushiki Kaisha Electrocardiographic waveform display apparatus, and method of expressing electrocardiographic waveforms
US4924875A (en) * 1987-10-09 1990-05-15 Biometrak Corporation Cardiac biopotential analysis system and method
US4928690A (en) * 1988-04-25 1990-05-29 Lifecor, Inc. Portable device for sensing cardiac function and automatically delivering electrical therapy
US4938228A (en) * 1989-02-15 1990-07-03 Righter William H Wrist worn heart rate monitor
US4951680A (en) * 1987-09-30 1990-08-28 National Research Development Corporation Fetal monitoring during labor
US4955382A (en) * 1984-03-06 1990-09-11 Ep Technologies Apparatus and method for recording monophasic action potentials from an in vivo heart
US4958641A (en) * 1989-03-10 1990-09-25 Instromedix, Inc. Heart data monitoring method and apparatus
US4972834A (en) * 1988-09-30 1990-11-27 Vitatron Medical B.V. Pacemaker with improved dynamic rate responsiveness
US4974162A (en) * 1987-03-13 1990-11-27 University Of Maryland Advanced signal processing methodology for the detection, localization and quantification of acute myocardial ischemia
US4974598A (en) * 1988-04-22 1990-12-04 Heart Map, Inc. EKG system and method using statistical analysis of heartbeats and topographic mapping of body surface potentials
US4977899A (en) * 1989-03-10 1990-12-18 Instromedix, Inc. Heart data monitoring method and apparatus
US4979510A (en) * 1984-03-06 1990-12-25 Ep Technologies, Inc. Apparatus and method for recording monophasic action potentials from an in vivo heart
US4989610A (en) * 1987-11-16 1991-02-05 Spacelabs, Inc. Method and system of ECG data review and analysis
US5000189A (en) * 1989-11-15 1991-03-19 Regents Of The University Of Michigan Method and system for monitoring electrocardiographic signals and detecting a pathological cardiac arrhythmia such as ventricular tachycardia
US5010888A (en) * 1988-03-25 1991-04-30 Arzco Medical Electronics, Inc. Method and apparatus for detection of posterior ischemia
US5020540A (en) * 1987-10-09 1991-06-04 Biometrak Corporation Cardiac biopotential analysis system and method
US5025795A (en) * 1989-06-28 1991-06-25 Kunig Horst E Non-invasive cardiac performance monitoring device and method
US5042497A (en) * 1990-01-30 1991-08-27 Cardiac Pacemakers, Inc. Arrhythmia prediction and prevention for implanted devices
US5090418A (en) * 1990-11-09 1992-02-25 Del Mar Avionics Method and apparatus for screening electrocardiographic (ECG) data
US5092341A (en) * 1990-06-18 1992-03-03 Del Mar Avionics Surface ecg frequency analysis system and method based upon spectral turbulence estimation
US5109862A (en) * 1990-03-19 1992-05-05 Del Mar Avionics Method and apparatus for spectral analysis of electrocardiographic signals
US5117833A (en) * 1990-11-13 1992-06-02 Corazonix Corporation Bi-spectral filtering of electrocardiogram signals to determine selected QRS potentials
US5117834A (en) * 1990-08-06 1992-06-02 Kroll Mark W Method and apparatus for non-invasively determing a patients susceptibility to ventricular arrhythmias
US5148812A (en) * 1991-02-20 1992-09-22 Georgetown University Non-invasive dynamic tracking of cardiac vulnerability by analysis of t-wave alternans
US5188116A (en) * 1991-02-28 1993-02-23 Vital Heart Systems, Inc. Electrocardiographic method and device
US5201321A (en) * 1991-02-11 1993-04-13 Fulton Keith W Method and apparatus for diagnosing vulnerability to lethal cardiac arrhythmias
US5234404A (en) * 1988-02-19 1993-08-10 Gensia Pharmaceuticals, Inc. Diagnosis, evaluation and treatment of coronary artery disease by exercise simulation using closed loop drug delivery of an exercise simulating agent beta agonist
US5253650A (en) * 1989-05-16 1993-10-19 Sharp Kabushiki Kaisha Apparatus for recording an electrocardiogram
US5265617A (en) * 1991-02-20 1993-11-30 Georgetown University Methods and means for non-invasive, dynamic tracking of cardiac vulnerability by simultaneous analysis of heart rate variability and T-wave alternans
US5277190A (en) * 1992-04-07 1994-01-11 The Board Of Regents Of The University Of Oklahoma Cycle length variability in nonsustained ventricular tachycardia
US5323783A (en) * 1992-11-12 1994-06-28 Del Mar Avionics Dynamic ST segment estimation and adjustment
US5343870A (en) * 1991-11-12 1994-09-06 Quinton Instrument Company Recorder unit for ambulatory ECG monitoring system
US5423878A (en) * 1984-03-06 1995-06-13 Ep Technologies, Inc. Catheter and associated system for pacing the heart
US5437285A (en) * 1991-02-20 1995-08-01 Georgetown University Method and apparatus for prediction of sudden cardiac death by simultaneous assessment of autonomic function and cardiac electrical stability
US5570696A (en) * 1994-01-26 1996-11-05 Cambridge Heart, Inc. Method and apparatus for assessing myocardial electrical stability
US5713367A (en) * 1994-01-26 1998-02-03 Cambridge Heart, Inc. Measuring and assessing cardiac electrical stability
US5792065A (en) * 1997-03-18 1998-08-11 Marquette Medical Systems, Inc. Method and apparatus for determining T-wave marker points during QT dispersion analysis
US5819741A (en) * 1994-10-07 1998-10-13 Ortivus Medical Ab Cardiac monitoring system and method
US5891045A (en) * 1996-07-17 1999-04-06 Cambridge Heart, Inc. Method and system for obtaining a localized cardiac measure
US5921940A (en) * 1991-02-20 1999-07-13 Georgetown University Method and apparatus for using physiologic stress in assessing myocardial electrical stability
US5935082A (en) * 1995-01-26 1999-08-10 Cambridge Heart, Inc. Assessing cardiac electrical stability
US6169919B1 (en) * 1999-05-06 2001-01-02 Beth Israel Deaconess Medical Center, Inc. System and method for quantifying alternation in an electrocardiogram signal
US6389308B1 (en) * 2000-05-30 2002-05-14 Vladimir Shusterman System and device for multi-scale analysis and representation of electrocardiographic data
US6453191B2 (en) * 2000-02-18 2002-09-17 Cambridge Heart, Inc. Automated interpretation of T-wave alternans results
US20020138106A1 (en) * 2000-10-23 2002-09-26 Christini David J. Intracardiac detection and control of repolarization alternans
US20030060724A1 (en) * 2001-07-13 2003-03-27 Srikanth Thiagarajan Method and apparatus for monitoring cardiac patients for T-wave alternans
US6668189B2 (en) * 2001-10-05 2003-12-23 Ge Medical Systems Information Technologies, Inc. Method and system for measuring T-wave alternans by alignment of alternating median beats to a cubic spline
US6850796B1 (en) * 1999-08-31 2005-02-01 David W. Mortara Method and apparatus to optimally measure cardiac depolarization/repolarization instability
US7072709B2 (en) * 2004-04-15 2006-07-04 Ge Medical Information Technologies, Inc. Method and apparatus for determining alternans data of an ECG signal

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3554187A (en) 1965-10-21 1971-01-12 Humetrics Corp Method and apparatus for automatically screening of electrocardiac signals
DE2604460C2 (en) 1976-02-05 1985-10-17 Michael S. 8113 Kochel Lampadius Arrangement for long-term monitoring of cardiac action potentials
GB2070871B (en) 1980-02-29 1984-08-30 Anderson J Pattern recognition
WO1981002832A1 (en) 1980-03-31 1981-10-15 Datamedix Inc Medical monitor
EP0080821A3 (en) 1981-11-16 1983-10-05 Datamedix, Inc. Dual channel ambulatory monitoring unit and system
DE3303104A1 (en) 1983-01-31 1984-08-02 Belorusskij naučno-issledovatel'skij institut kardiologii, Minsk Device for monitoring cardiac action
FR2539978A1 (en) 1983-01-31 1984-08-03 Bruss I Kardiolog Apparatus for monitoring cardiac activity
DD287649A5 (en) 1989-09-08 1991-03-07 ���@���������������`@���@������������@ �������@������ k�� CIRCUIT ARRANGEMENT FOR CLASSIFYING THE CHANGING SPEED OF THE SIGNAL SIZE HEART RATE
US6463320B1 (en) * 1999-12-22 2002-10-08 Ge Medical Systems Information Technologies, Inc. Clinical research workstation
US6760615B2 (en) * 2001-10-31 2004-07-06 Medtronic, Inc. Method and apparatus for discriminating between tachyarrhythmias
US7027857B2 (en) * 2003-02-14 2006-04-11 The General Electric Company Method and system for improved measurement of T-wave alternans

Patent Citations (84)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3658055A (en) * 1968-05-20 1972-04-25 Hitachi Ltd Automatic arrhythmia diagnosing system
US3544187A (en) * 1968-10-23 1970-12-01 Sperry Rand Corp Filing cabinet
US3759248A (en) * 1971-02-08 1973-09-18 Spacelabs Inc Cardiac arrythmia detector
US3821948A (en) * 1971-11-03 1974-07-02 Hoffmann La Roche System and method for analyzing absolute derivative signal from heartbeat
US3902479A (en) * 1973-02-16 1975-09-02 Hoffmann La Roche Method and apparatus for heartbeat rate monitoring
US3952731A (en) * 1973-12-15 1976-04-27 Ferranti Limited Cardiac monitoring apparatus
US4124894A (en) * 1974-10-15 1978-11-07 Hycel, Inc. Apparatus and method for reporting detected error in a cardiac signal
US4202340A (en) * 1975-09-30 1980-05-13 Mieczyslaw Mirowski Method and apparatus for monitoring heart activity, detecting abnormalities, and cardioverting a malfunctioning heart
US4136690A (en) * 1977-10-31 1979-01-30 Del Mar Avionics Method and apparatus for vector analysis of ECG arrhythmias
US4170992A (en) * 1978-01-05 1979-10-16 Hewlett-Packard Company Fiducial point location
US4181135A (en) * 1978-03-03 1980-01-01 American Optical Corporation Method and apparatus for monitoring electrocardiographic waveforms
US4457315A (en) * 1978-09-18 1984-07-03 Arvin Bennish Cardiac arrhythmia detection and recording
US4316249A (en) * 1979-09-28 1982-02-16 Hittman Corporation Automatic high speed Holter scanning system
US4417306A (en) * 1980-01-23 1983-11-22 Medtronic, Inc. Apparatus for monitoring and storing utilizing a data processor
US4422459A (en) * 1980-11-18 1983-12-27 University Patents, Inc. Electrocardiographic means and method for detecting potential ventricular tachycardia
US4458692A (en) * 1982-02-11 1984-07-10 Arrhythmia Research Technology, Inc. System and method for predicting ventricular tachycardia with a gain controlled high pass filter
US4458691A (en) * 1982-02-11 1984-07-10 Arrhythmia Research Technology, Inc. System and method for predicting ventricular tachycardia by adaptive high pass filter
US4492235A (en) * 1982-02-11 1985-01-08 Arrhythmia Research Technology, Inc. System and method for predicting ventricular tachycardia by derivative analysis
US4432375A (en) * 1982-05-24 1984-02-21 Cardiac Resuscitator Corporation Cardiac arrhythmia analysis system
US4475558A (en) * 1982-05-28 1984-10-09 Healthdyne, Inc. System for providing short-term event data and long-term trend data
US4519395A (en) * 1982-12-15 1985-05-28 Hrushesky William J M Medical instrument for noninvasive measurement of cardiovascular characteristics
US4665485A (en) * 1983-07-22 1987-05-12 Lundy Research Laboratories, Inc. Method and apparatus for characterizing the unknown state of a physical system
US4583553A (en) * 1983-11-15 1986-04-22 Medicomp, Inc. Ambulatory ECG analyzer and recorder
US5423878A (en) * 1984-03-06 1995-06-13 Ep Technologies, Inc. Catheter and associated system for pacing the heart
US4979510A (en) * 1984-03-06 1990-12-25 Ep Technologies, Inc. Apparatus and method for recording monophasic action potentials from an in vivo heart
US4955382A (en) * 1984-03-06 1990-09-11 Ep Technologies Apparatus and method for recording monophasic action potentials from an in vivo heart
US4680708A (en) * 1984-03-20 1987-07-14 Washington University Method and apparatus for analyzing electrocardiographic signals
US4603703A (en) * 1984-04-13 1986-08-05 The Board Of Trustees Of The Leland Stanford Junior University Method for real-time detection and identification of neuroelectric signals
US4589420A (en) * 1984-07-13 1986-05-20 Spacelabs Inc. Method and apparatus for ECG rhythm analysis
US4679144A (en) * 1984-08-21 1987-07-07 Q-Med, Inc. Cardiac signal real time monitor and method of analysis
US4796638A (en) * 1984-09-28 1989-01-10 Kabushiki Kaisya Advance Kaihatsu Kenkyujo Artifact detecting apparatus in the measurement of a biological signal
US4616659A (en) * 1985-05-06 1986-10-14 At&T Bell Laboratories Heart rate detection utilizing autoregressive analysis
US4832038A (en) * 1985-06-05 1989-05-23 The Board Of Trustees Of University Of Illinois Apparatus for monitoring cardiovascular regulation using heart rate power spectral analysis
US4802491A (en) * 1986-07-30 1989-02-07 Massachusetts Institute Of Technology Method and apparatus for assessing myocardial electrical stability
US4732157A (en) * 1986-08-18 1988-03-22 Massachusetts Institute Of Technology Method and apparatus for quantifying beat-to-beat variability in physiologic waveforms
US4974162A (en) * 1987-03-13 1990-11-27 University Of Maryland Advanced signal processing methodology for the detection, localization and quantification of acute myocardial ischemia
US4951680A (en) * 1987-09-30 1990-08-28 National Research Development Corporation Fetal monitoring during labor
US4924875A (en) * 1987-10-09 1990-05-15 Biometrak Corporation Cardiac biopotential analysis system and method
US5020540A (en) * 1987-10-09 1991-06-04 Biometrak Corporation Cardiac biopotential analysis system and method
US4989610A (en) * 1987-11-16 1991-02-05 Spacelabs, Inc. Method and system of ECG data review and analysis
US4896677A (en) * 1987-12-26 1990-01-30 Fukuda Denshi Kabushiki Kaisha Electrocardiographic waveform display apparatus, and method of expressing electrocardiographic waveforms
US5234404A (en) * 1988-02-19 1993-08-10 Gensia Pharmaceuticals, Inc. Diagnosis, evaluation and treatment of coronary artery disease by exercise simulation using closed loop drug delivery of an exercise simulating agent beta agonist
US4854327A (en) * 1988-03-07 1989-08-08 Kunig Horst E Non-invasive and continuous cardiac performance monitoring device
US5010888A (en) * 1988-03-25 1991-04-30 Arzco Medical Electronics, Inc. Method and apparatus for detection of posterior ischemia
US4974598A (en) * 1988-04-22 1990-12-04 Heart Map, Inc. EKG system and method using statistical analysis of heartbeats and topographic mapping of body surface potentials
US4928690A (en) * 1988-04-25 1990-05-29 Lifecor, Inc. Portable device for sensing cardiac function and automatically delivering electrical therapy
US4860762A (en) * 1988-06-03 1989-08-29 Hewlett-Packard Company Dual channel resolver for real time arrythmia analysis
US4972834A (en) * 1988-09-30 1990-11-27 Vitatron Medical B.V. Pacemaker with improved dynamic rate responsiveness
US4938228A (en) * 1989-02-15 1990-07-03 Righter William H Wrist worn heart rate monitor
US4958641A (en) * 1989-03-10 1990-09-25 Instromedix, Inc. Heart data monitoring method and apparatus
US4977899A (en) * 1989-03-10 1990-12-18 Instromedix, Inc. Heart data monitoring method and apparatus
US5253650A (en) * 1989-05-16 1993-10-19 Sharp Kabushiki Kaisha Apparatus for recording an electrocardiogram
US5025795A (en) * 1989-06-28 1991-06-25 Kunig Horst E Non-invasive cardiac performance monitoring device and method
US5000189A (en) * 1989-11-15 1991-03-19 Regents Of The University Of Michigan Method and system for monitoring electrocardiographic signals and detecting a pathological cardiac arrhythmia such as ventricular tachycardia
US5042497A (en) * 1990-01-30 1991-08-27 Cardiac Pacemakers, Inc. Arrhythmia prediction and prevention for implanted devices
US5109862A (en) * 1990-03-19 1992-05-05 Del Mar Avionics Method and apparatus for spectral analysis of electrocardiographic signals
US5092341A (en) * 1990-06-18 1992-03-03 Del Mar Avionics Surface ecg frequency analysis system and method based upon spectral turbulence estimation
US5117834A (en) * 1990-08-06 1992-06-02 Kroll Mark W Method and apparatus for non-invasively determing a patients susceptibility to ventricular arrhythmias
US5090418A (en) * 1990-11-09 1992-02-25 Del Mar Avionics Method and apparatus for screening electrocardiographic (ECG) data
US5117833A (en) * 1990-11-13 1992-06-02 Corazonix Corporation Bi-spectral filtering of electrocardiogram signals to determine selected QRS potentials
US5201321A (en) * 1991-02-11 1993-04-13 Fulton Keith W Method and apparatus for diagnosing vulnerability to lethal cardiac arrhythmias
US5265617A (en) * 1991-02-20 1993-11-30 Georgetown University Methods and means for non-invasive, dynamic tracking of cardiac vulnerability by simultaneous analysis of heart rate variability and T-wave alternans
US5560370A (en) * 1991-02-20 1996-10-01 Georgetown University Method and apparatus for prediction of cardiac electrical instability by simultaneous assessment of T-wave alternans and QT interval dispersion
US5921940A (en) * 1991-02-20 1999-07-13 Georgetown University Method and apparatus for using physiologic stress in assessing myocardial electrical stability
US5148812A (en) * 1991-02-20 1992-09-22 Georgetown University Non-invasive dynamic tracking of cardiac vulnerability by analysis of t-wave alternans
US5437285A (en) * 1991-02-20 1995-08-01 Georgetown University Method and apparatus for prediction of sudden cardiac death by simultaneous assessment of autonomic function and cardiac electrical stability
US5188116A (en) * 1991-02-28 1993-02-23 Vital Heart Systems, Inc. Electrocardiographic method and device
US5343870A (en) * 1991-11-12 1994-09-06 Quinton Instrument Company Recorder unit for ambulatory ECG monitoring system
US5277190A (en) * 1992-04-07 1994-01-11 The Board Of Regents Of The University Of Oklahoma Cycle length variability in nonsustained ventricular tachycardia
US5323783A (en) * 1992-11-12 1994-06-28 Del Mar Avionics Dynamic ST segment estimation and adjustment
US5713367A (en) * 1994-01-26 1998-02-03 Cambridge Heart, Inc. Measuring and assessing cardiac electrical stability
US5570696A (en) * 1994-01-26 1996-11-05 Cambridge Heart, Inc. Method and apparatus for assessing myocardial electrical stability
US5819741A (en) * 1994-10-07 1998-10-13 Ortivus Medical Ab Cardiac monitoring system and method
US5935082A (en) * 1995-01-26 1999-08-10 Cambridge Heart, Inc. Assessing cardiac electrical stability
US5891045A (en) * 1996-07-17 1999-04-06 Cambridge Heart, Inc. Method and system for obtaining a localized cardiac measure
US5792065A (en) * 1997-03-18 1998-08-11 Marquette Medical Systems, Inc. Method and apparatus for determining T-wave marker points during QT dispersion analysis
US6169919B1 (en) * 1999-05-06 2001-01-02 Beth Israel Deaconess Medical Center, Inc. System and method for quantifying alternation in an electrocardiogram signal
US6850796B1 (en) * 1999-08-31 2005-02-01 David W. Mortara Method and apparatus to optimally measure cardiac depolarization/repolarization instability
US6453191B2 (en) * 2000-02-18 2002-09-17 Cambridge Heart, Inc. Automated interpretation of T-wave alternans results
US6389308B1 (en) * 2000-05-30 2002-05-14 Vladimir Shusterman System and device for multi-scale analysis and representation of electrocardiographic data
US20020138106A1 (en) * 2000-10-23 2002-09-26 Christini David J. Intracardiac detection and control of repolarization alternans
US20030060724A1 (en) * 2001-07-13 2003-03-27 Srikanth Thiagarajan Method and apparatus for monitoring cardiac patients for T-wave alternans
US6668189B2 (en) * 2001-10-05 2003-12-23 Ge Medical Systems Information Technologies, Inc. Method and system for measuring T-wave alternans by alignment of alternating median beats to a cubic spline
US7072709B2 (en) * 2004-04-15 2006-07-04 Ge Medical Information Technologies, Inc. Method and apparatus for determining alternans data of an ECG signal

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102512157A (en) * 2011-12-15 2012-06-27 重庆大学 Dynamic electrocardiogram T wave alternate quantitative analysis method based on models
US9149201B2 (en) 2012-03-30 2015-10-06 Nihon Kohden Corporation TWA measuring apparatus and TWA measuring method
CN103431857A (en) * 2013-09-09 2013-12-11 苏州百慧华业精密仪器有限公司 Method for automatically scanning suspicious T wave alteration (TWA) positive sections of Holter

Also Published As

Publication number Publication date
US7072709B2 (en) 2006-07-04
US20050234363A1 (en) 2005-10-20
US20060173372A1 (en) 2006-08-03
US8068900B2 (en) 2011-11-29
CN1682654A (en) 2005-10-19

Similar Documents

Publication Publication Date Title
US8068900B2 (en) Method and apparatus for determining alternans data of an ECG signal
US7509159B2 (en) Method and apparatus for detecting cardiac repolarization abnormality
Xue et al. Algorithms for computerized QT analysis
US10478084B2 (en) Electrocardiogram signal detection
Li et al. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter
US8321005B2 (en) System for continuous cardiac pathology detection and characterization
EP2688468A1 (en) Apparatus and method for measuring physiological signal quality
CN110226919B (en) Electrocardiosignal type detection method and device, computer equipment and storage medium
WO2001061550A2 (en) Automated interpretation of t-wave alternans results
US6668189B2 (en) Method and system for measuring T-wave alternans by alignment of alternating median beats to a cubic spline
CN111091116A (en) Signal processing method and system for judging arrhythmia
Xie et al. Bidirectional recurrent neural network and convolutional neural network (BiRCNN) for ECG beat classification
CN110367936B (en) Electrocardiosignal detection method and device
US5509425A (en) Arrangement for and method of diagnosing and warning of a heart attack
US5649544A (en) Method of and arrangement for diagnosing heart disease
US20050234353A1 (en) Method and apparatus for analysis of non-invasive cardiac parameters
Reddy et al. Ecg signal characterization and correlation to heart abnormalities
US7187966B2 (en) Method and apparatus for displaying alternans data
US7027857B2 (en) Method and system for improved measurement of T-wave alternans
CN109394206B (en) Real-time monitoring method and device based on premature beat signal in wearable electrocardiosignal
CN105050493B (en) For determining the apparatus and method of the appearance of the QRS complex in ECG data
US20040148109A1 (en) Method and apparatus for prediction of cardiac dysfunction
Islam et al. Resampling of ECG signal for improved morphology alignment
Tun et al. Analysis of computer aided identification system for ECG characteristic points
Mayapur Detection and Processing of the R Peak

Legal Events

Date Code Title Description
STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION