CN101461711A - Shockable rhythm recognition algorithm based on standard deviation of standard slope absolute value - Google Patents
Shockable rhythm recognition algorithm based on standard deviation of standard slope absolute value Download PDFInfo
- Publication number
- CN101461711A CN101461711A CNA2009100451541A CN200910045154A CN101461711A CN 101461711 A CN101461711 A CN 101461711A CN A2009100451541 A CNA2009100451541 A CN A2009100451541A CN 200910045154 A CN200910045154 A CN 200910045154A CN 101461711 A CN101461711 A CN 101461711A
- Authority
- CN
- China
- Prior art keywords
- absolute value
- standard deviation
- slope absolute
- heart
- rhythm
- 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.)
- Pending
Links
Images
Abstract
A recognition algorithm of heart rhythm capable of electric shock cardioversion based on standard deviation fo the standardized slope absolute value, is suitable for disease diagnosis and treatment instrument or apparatus, including the steps: S1, preprocessing the electrocardiosignal; S2, recognizing whether the electrocardiosignal is the heart rhythm of cardiac arrest, if so, judging the heart rhythm as the heart rhythm incapable of electric shock cardioversion, otherwise, continuing the follow-up steps S3 and S4; S3, calculating the standard deviation of the standardized slope absolute value; S4, determining whether it pertains to the heart rhythm incapable of electric shock cardioversion or the heart rhythm capable of electric shock cardioversion based on the standard deviation of the standardized slope absolute value. The invention improves the sensitivity and specificity of recognizing the heart rhythm capable of electric shock cardioversion, simplifies the computational complexity of the algorithm, can be applied to the existing ECG guardianship equipment and automatic external defibrillator, and the like instruments requiring body surface electrocardiogram for recognizing the heart rhythm capable of electric shock cardioversion.
Description
Technical field
The present invention relates to a kind of electrocardiosignal (ECG) recognition methods, but the particularly a kind of Electrical Cardioversion rhythm of the heart (Shockable Rhythm, ShR) recognizer of improving existing electrocardiogram monitor and automated external defibrillator performance.
Background technology
Sudden cardiac death (SCD) is meant the natural death of the unexpected generation that causes owing to the heart reason.The reason major part that causes sudden cardiac death is momentary dysfunction and the electrophysiological change that takes place on all kinds of cardiovascular pathological changes basis, and cause that malignant ventricular arrhythmia such as ventricular tachycardia (are called for short chamber speed, VT), ventricular fibrillation (is called for short the chamber and quivers, VF) etc.Electric defibrillation is the first-selected effective ways that stop most rapidity malignant ventricular arrhythmias.
1997, American Heart Association (AHA) has delivered a suggestion relevant with automated external defibrillator (AED) algorithm performance report " automated external defibrillator that is used for the public arena defibrillation: to illustrating and the performance of the arrhythmia analysis algorithm of report on (Circulation) magazine in circulation, comprise the suggestion of new waveform and raising safety " (" Automatic External Defibrillators forPublic Access Defibrillation:Recommendations for Specifying and ReportingArrhythmia Analysis Algorithm Performance, Incorporating New Waveforms, and Enhancing Safety. ").
This suggestion is divided into following three major types with the rhythm of the heart: but the Electrical Cardioversion rhythm of the heart (shockable rhythms, ShR), can not the Electrical Cardioversion rhythm of the heart (nonshockable rhythms, NShR) and the middle rhythm of the heart (Intermediate rhythms).
At present but the Electrical Cardioversion rhythm recognition algorithm of bibliographical information exists variety of issue, as since the chamber when quivering Electrocardiographic form can change a lot, but various algorithm based on ECG R wave identification is not suitable for the differentiation of the Electrical Cardioversion rhythm of the heart; Phase space rebuild (Phase Space ReconstructionAlgorithm, PSR) algorithm, signal comparison algorithm (Signal Comparison Algorithm, though SCA) wait very high specificity is arranged, sensitivity is very poor; And some are based on the algorithm computation complexity of various conversion and analysis of complexity, to having relatively high expectations of hardware.So, but the differentiation algorithm of the existing Electrical Cardioversion rhythm of the heart still exists sensitivity and specificity not to take into account, or problem such as calculation of complex, for example, as typical example, also there are some such shortcomings in the HILB algorithm application in the instrument or device of the diagnosis and treatment of disease, the HILB algorithm has used method-Hilbert transform method of often using when analyzing nonlinear properties to make up phase space.Suppose that electrocardiosignal is x (t), obtain x after it is done Hilbert transform
H(t), if, use x with x (t) expression x axial coordinate
H(t) represent the y axial coordinate, just constructed the phase space of a two dimension.In such phase space, the track of chaotic signal can be more mixed and disorderly than the track of rule signal.People such as Anoton, Robert and Karl find that the trajectory of phase space of VF signal is more mixed and disorderly than the trajectory of phase space of SR (sinus rhythm) signal.So they suppose that the VF signal is a chaos, and the SR signal is a rule.They are divided into the grid of 40 identical sizes of 40 x with the phase space that builds, and the grid of the trajectory of phase space process of statistics electrocardiosignal is counted.Because the SR signal is a rule, the VF signal is a chaos, so compare with the trajectory of phase space of SR signal, the trajectory of phase space of VF signal can pass through more grid.
In order to reduce amount of calculation, also need signal is done down-sampled.
The detailed process of HILB algorithm is as follows:
1. down-sampled with 50Hz to signal.
2. the Hilbert transform of electrocardiosignal x (t) is x
H(t), make up the phase space of 40 x, 40 lattice, calculate (x (t), x
H(t)) shared lattice are counted visited boxes in constructed phase space.
3. definition
And to get threshold value be d0,
If d〉d0, then be judged to VF;
If d＜=d0 then is judged to SR.
Summary of the invention:
As mentioned above, but for electrocardiogram monitor and automated external defibrillator provide the Electrical Cardioversion rhythm recognition algorithm of discriminant accuracy height and fast operation, be technical problem to be solved by this invention.For this reason, but the object of the present invention is to provide a kind of discern accurately, calculate simple, can satisfy application requirements, based on the Electrical Cardioversion rhythm recognition algorithm of standard slope absolute value standard deviation, but to improve the existing performance that needs to use the instrument and equipment of Electrical Cardioversion rhythm of the heart recognition methods.
Technical scheme of the present invention is as follows:
But the Electrical Cardioversion rhythm recognition algorithm of a kind of standard slope absolute value standard deviation that proposes according to the present invention comprises that step is as follows:
At first, electrocardiosignal is carried out the identification of the asystole rhythm of the heart:
If the asystole rhythm of the heart then is judged to NShR;
If not the asystole rhythm of the heart, then carry out the step of back.
Normalized slope absolute value standard deviation;
Differentiate NShR and ShR according to standard slope absolute value standard deviation,
Discrimination standard is:
If standard slope absolute value standard deviation 〉=threshold value, then be judged to NShR;
If standard slope absolute value standard deviation＜threshold value then is judged to ShR.
The detailed process of the above-mentioned identification asystole rhythm of the heart is:
Amplitude is judged to the asystole rhythm of the heart less than the electrocardiosignal of 80uV.
The detailed process of aforementioned calculation standard slope absolute value standard deviation is:
At first, one section electrocardiogram (ECG) data is divided into segment by identical interval, each segment is called a grizzly bar (bar), and each interval is called grill width (barwidth);
Then, calculate the absolute value (slope) of the difference of last interior sampling point of each grizzly bar and first sampling point, i.e. slope
i=abs (signal
i(barwidth)-signal
i(i)), slope wherein
iThe slope absolute value of representing i grizzly bar, signal
iRepresent the sampling point sequence in i the grizzly bar;
Then, calculate the standard deviation (slope_std) of all slope absolute values;
At last, to the slope_std standardization, promptly slope_std/mean (slope) obtains standard slope absolute value standard deviation (slope_stdnor).
Owing to adopted above technical scheme, but improved the sensitivity and the specificity of the identification Electrical Cardioversion rhythm of the heart.Also simplified the computation complexity of algorithm in addition.The present invention can be applicable to electrocardiogram monitor and automated external defibrillator (AED) but etc. need be according to the instrument and equipment of the surface electrocardiogram identification Electrical Cardioversion rhythm of the heart.
Description of drawings:
Fig. 1 is main process figure of the present invention.
Fig. 2 is the flow chart of " S1 pretreatment " step among the main process figure of the present invention.
Fig. 3 is the flow chart of " S3 normalized slope absolute value standard deviation " step among the main process figure of the present invention.
The specific embodiment:
The invention will be further described below by specific embodiment.
Present embodiment is that the present invention is at personal computer (PC) and matrix experiment chamber (MatrixLaboratory, Matlab) a kind of possible realization on the platform, and on the test data set that constitutes by three standard databases of the arrhythmia data base of Massachusetts Polytechnics (MITDB), the ventricular arrhythmia data base of Ke Laideng university (CUDB), the malignant ventricular arrhythmia data base of Massachusetts Polytechnics (VFDB), test and compare.The present embodiment concrete steps are as follows:
1. electrocardiosignal is carried out pretreatment:
A) moving average filter on one 5 rank of use, high-frequency noises such as filtering spread noise and myoelectricity noise;
B) use the high pass filter of a cut-off frequency, suppress baseline drift as 1Hz;
C) use the Butterworth low pass filter of a cut-off frequency, further the irrelevant radio-frequency component of filtering as 30Hz.
2. electrocardiosignal is carried out the identification of the asystole rhythm of the heart:
If the amplitude of electrocardiosignal less than 80uV, is then thought the asystole rhythm of the heart, be judged to NShR;
Not the asystole rhythm of the heart if the amplitude of electrocardiosignal more than or equal to 80uV, is then thought, continue the step of back.
3. normalized slope absolute value standard deviation:
A) one section electrocardiogram (ECG) data is divided into segment by identical interval, each segment is called a grizzly bar (bar), and interval is called grill width (barwidth), and barwidth is taken as 16ms (when sample rate is 250Hz, corresponding to 4 sampled points);
B) calculate the absolute value (slope) of the difference of last sampling point in each grizzly bar and first sampling point, i.e. slope
i=abs (signal
i(barwidth)-signal
i(i)), slope wherein
iThe slope absolute value of representing i grizzly bar, signal
iRepresent the sampling point sequence in i the grizzly bar;
C) calculate the standard deviation (slope_std) of all slope absolute values;
D) to the slope_std standardization, promptly slope_std/mean (slope) obtains standard slope absolute value standard deviation (slope_stdnor).
4. differentiate NShR and ShR according to standard slope absolute value standard deviation:
Discrimination standard is:
If standard slope absolute value standard deviation 〉=threshold value T, then be judged to NShR;
If standard slope absolute value standard deviation＜threshold value T then is judged to ShR.
The software and hardware configuration that present embodiment uses is as follows:
-hardware: Dell is to 4 computers, dominant frequency 226GHz, 512,000,000 internal memories (Dell OPTIPLEXGX270, Pentium (R) 4 (2.26GHz) and 512 MB DDR SDRAM)
-software: MATLAB R13, " signal processing workbox " version 6.0 (＂ Signal ProcessingToolbox ＂ version 6.0)
Under following test condition, to present embodiment and prior art Hilbert (HILB) algorithm
[1] [2]Test and compare:
Test data set is all data of MITDB, CUDB, three standard databases of VFDB, is a segment (sample data) with 8s, and adjacent two segment zero-times differ 1s.
The goldstandard (Golden Standard) of rhythm of the heart classification:
A) the reference note that carries according to the data base (reference annotation) carries out rhythm of the heart classification to the data segment.
B) ShR: the rhythm of the heart (rhythm) class annotation information is labeled as the electrocardiogram (ECG) data of VF, VT,
NShR: other all rhythms of the heart;
C) containing the segment of mixing the rhythm of the heart does not use.
Test result such as following table:
Wherein, AUC is meant and receives operating characteristic curve (ROC) area down
[3] [4], be concentrated expression sensitivity and specific index.
By in the table as seen, the AUC of present embodiment (0.980) is greater than the AUC (0.965) of HILB algorithm, and remarkable on this difference statistical significance
。The classification performance that present embodiment is described is better than the HILB algorithm.And also be less than the HILB algorithm computation time of present embodiment.
If threshold value T is taken as 0.98, but in the present embodiment based on the sensitivity of the Electrical Cardioversion rhythm recognition algorithm of standard slope absolute value standard deviation be 92.0%, specificity is 95%, reaches the sensitivity 90% that AHA advises, the performance requirement of specificity 95%.
*List of references of the present invention
[1]DI?Robert?Tratnig.Reliability?of?New?Fibrillation?DetectionAlgorithms?for?Automated?External?Defibrillators[D].Dornbirn，Austria：Technische?Universit"at?Graz，2005.
[2]A.Amann，R.Tratnig，K.Unterkofler.A?new?ventricular?fibrillationdetection?algorithm?for?automated?external?defibrillators[J].Computers?inCardiology，2005：559-562.
[3] JP Marques work, Wu Yifei translates. pattern recognition---principle, method and application [M]. and publishing house of Tsing-Hua University, 2002:113-115.
[4] space passes China, Xu Yongyong. and non parametric method is estimated ROC area under curve [J]. Chinese health statistics, 1999,16 (4): 241-244.
[5]Richard?E.Kerber，Chair?MD，Lance?B.Becker，et?al.AutomaticExternal?Defibrillators?for?Public?Access?Defibrillation：Recommendationsfor?Specifying?and?Reporting?Arrhythmia?Analysis?Algorithm?Performance，Incorporating?New?Waveforms，and?Enhancing?Safety[J].Circulation，1997，95(6)：1677-1682.
Claims (5)
1. but Electrical Cardioversion rhythm recognition algorithm based on standard slope absolute value standard deviation is applicable to the diagnosis and treatment instrument or the device of disease to comprise step:
S1. the electrocardiosignal that collects is carried out pretreatment;
S2. electrocardiosignal is carried out the identification of the asystole rhythm of the heart: if the asystole rhythm of the heart, then being judged to can not the Electrical Cardioversion rhythm of the heart; If not the asystole rhythm of the heart then continues to carry out subsequent step S3, S4;
S3. normalized slope absolute value standard deviation;
But S4. distinguish whether be can not the Electrical Cardioversion rhythm of the heart or the Electrical Cardioversion rhythm of the heart, if standard slope absolute value standard deviation according to standard slope absolute value standard deviation 〉=threshold value, then being judged to can not the Electrical Cardioversion rhythm of the heart; If standard slope absolute value standard deviation＜threshold value, but then be judged to the Electrical Cardioversion rhythm of the heart.
2. but the Electrical Cardioversion rhythm recognition algorithm based on standard slope absolute value standard deviation according to claim 1 is characterized in that, described electrocardiosignal pretreatment comprises step:
S11. use the moving average filter on one 5 rank, filter away high frequency noise;
S12. use the high pass filter of a cut-off frequency, suppress baseline drift as 1Hz;
S13. use the Butterworth low pass filter of a cut-off frequency, further the irrelevant radio-frequency component of filtering as 30Hz.
But 3. according to claim 1 and 2 based on the recognition methods of the standard slope absolute value standard deviation Electrical Cardioversion rhythm of the heart, it is characterized in that described step S11 filter away high frequency noise comprises spread noise and myoelectricity noise.
4. but the Electrical Cardioversion rhythm recognition algorithm based on standard slope absolute value standard deviation according to claim 1 is characterized in that the described asystole rhythm of the heart is meant that the electrocardiosignal amplitude is less than 80uV.
5. but the Electrical Cardioversion rhythm recognition algorithm based on standard slope absolute value standard deviation according to claim 1 is characterized in that, described normalized slope absolute value standard deviation comprises step:
S31. one section electrocardiogram (ECG) data is divided into segment by identical interval, each segment is called a grizzly bar, each interval is called grill width;
S32. calculate the absolute value of the difference of last interior sampling point of each grizzly bar and first sampling point, form the slope absolute value sequence;
S33. calculate the standard deviation of all slope absolute values;
S34. with the meansigma methods of standard deviation, try to achieve standard slope absolute value standard deviation divided by all slope absolute values.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2009100451541A CN101461711A (en) | 2009-01-12 | 2009-01-12 | Shockable rhythm recognition algorithm based on standard deviation of standard slope absolute value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2009100451541A CN101461711A (en) | 2009-01-12 | 2009-01-12 | Shockable rhythm recognition algorithm based on standard deviation of standard slope absolute value |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101461711A true CN101461711A (en) | 2009-06-24 |
Family
ID=40802654
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNA2009100451541A Pending CN101461711A (en) | 2009-01-12 | 2009-01-12 | Shockable rhythm recognition algorithm based on standard deviation of standard slope absolute value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101461711A (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8165666B1 (en) | 2011-05-02 | 2012-04-24 | Topera, Inc. | System and method for reconstructing cardiac activation information |
| CN104382590A (en) * | 2014-12-11 | 2015-03-04 | 赖大坤 | Automatic shockable rhythm identification and classification method combined with electrocardio time-frequency domain feature analysis |
| CN106691437A (en) * | 2017-01-26 | 2017-05-24 | 浙江铭众科技有限公司 | Fetal heart rate extraction method based on maternal electrocardiosignals |
| CN106889981A (en) * | 2017-01-26 | 2017-06-27 | 浙江铭众科技有限公司 | A kind of intelligent terminal for extracting fetal heart frequency |
| US9913615B2 (en) | 2011-05-02 | 2018-03-13 | The Regents Of The University Of California | System and method for reconstructing cardiac activation information |
| US9955879B2 (en) | 2008-10-09 | 2018-05-01 | The Regents Of The University Of California | System for analysis of complex rhythm disorders |
| US10058262B2 (en) | 2011-12-09 | 2018-08-28 | The Regents Of The University Of California | System and method of identifying sources for biological rhythms |
| US10085655B2 (en) | 2013-03-15 | 2018-10-02 | The Regents Of The University Of California | System and method to define drivers of sources associated with biological rhythm disorders |
| US10136860B2 (en) | 2008-05-13 | 2018-11-27 | The Regents Of The University Of California | System for detecting and treating heart instability |
| CN109171708A (en) * | 2018-10-25 | 2019-01-11 | 广东工业大学 | One kind can defibrillation rhythm of the heart identification device |
| US10271786B2 (en) | 2011-05-02 | 2019-04-30 | The Regents Of The University Of California | System and method for reconstructing cardiac activation information |
| US10398326B2 (en) | 2013-03-15 | 2019-09-03 | The Regents Of The University Of California | System and method of identifying sources associated with biological rhythm disorders |
| US10434319B2 (en) | 2009-10-09 | 2019-10-08 | The Regents Of The University Of California | System and method of identifying sources associated with biological rhythm disorders |
| US10485438B2 (en) | 2011-05-02 | 2019-11-26 | The Regents Of The University Of California | System and method for targeting heart rhythm disorders using shaped ablation |
| US10856760B2 (en) | 2010-04-08 | 2020-12-08 | The Regents Of The University Of California | Method and system for detection of biological rhythm disorders |
-
2009
- 2009-01-12 CN CNA2009100451541A patent/CN101461711A/en active Pending
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10136860B2 (en) | 2008-05-13 | 2018-11-27 | The Regents Of The University Of California | System for detecting and treating heart instability |
| US9955879B2 (en) | 2008-10-09 | 2018-05-01 | The Regents Of The University Of California | System for analysis of complex rhythm disorders |
| US11147462B2 (en) | 2008-10-09 | 2021-10-19 | The Regents Of The University Of California | Method for analysis of complex rhythm disorders |
| US10092196B2 (en) | 2008-10-09 | 2018-10-09 | The Regents Of The University Of California | Method for analysis of complex rhythm disorders |
| US10434319B2 (en) | 2009-10-09 | 2019-10-08 | The Regents Of The University Of California | System and method of identifying sources associated with biological rhythm disorders |
| US10856760B2 (en) | 2010-04-08 | 2020-12-08 | The Regents Of The University Of California | Method and system for detection of biological rhythm disorders |
| US10271786B2 (en) | 2011-05-02 | 2019-04-30 | The Regents Of The University Of California | System and method for reconstructing cardiac activation information |
| US8165666B1 (en) | 2011-05-02 | 2012-04-24 | Topera, Inc. | System and method for reconstructing cardiac activation information |
| US8594777B2 (en) | 2011-05-02 | 2013-11-26 | The Reagents Of The University Of California | System and method for reconstructing cardiac activation information |
| US9913615B2 (en) | 2011-05-02 | 2018-03-13 | The Regents Of The University Of California | System and method for reconstructing cardiac activation information |
| US10485438B2 (en) | 2011-05-02 | 2019-11-26 | The Regents Of The University Of California | System and method for targeting heart rhythm disorders using shaped ablation |
| US10149622B2 (en) | 2011-05-02 | 2018-12-11 | The Regents Of The University Of California | System and method for reconstructing cardiac activation information |
| US10058262B2 (en) | 2011-12-09 | 2018-08-28 | The Regents Of The University Of California | System and method of identifying sources for biological rhythms |
| US10398326B2 (en) | 2013-03-15 | 2019-09-03 | The Regents Of The University Of California | System and method of identifying sources associated with biological rhythm disorders |
| US10271744B2 (en) | 2013-03-15 | 2019-04-30 | The Regents Of The University Of California | System and method to identify sources associated with biological rhythm disorders |
| US10098560B2 (en) | 2013-03-15 | 2018-10-16 | The Regents Of The University Of California | System and method to identify sources associated with biological rhythm disorders |
| US10085655B2 (en) | 2013-03-15 | 2018-10-02 | The Regents Of The University Of California | System and method to define drivers of sources associated with biological rhythm disorders |
| US11446506B2 (en) | 2013-03-15 | 2022-09-20 | The Regents Of The University Of California | System and method of identifying sources associated with biological rhythm disorders |
| CN104382590A (en) * | 2014-12-11 | 2015-03-04 | 赖大坤 | Automatic shockable rhythm identification and classification method combined with electrocardio time-frequency domain feature analysis |
| CN106889981A (en) * | 2017-01-26 | 2017-06-27 | 浙江铭众科技有限公司 | A kind of intelligent terminal for extracting fetal heart frequency |
| CN106691437A (en) * | 2017-01-26 | 2017-05-24 | 浙江铭众科技有限公司 | Fetal heart rate extraction method based on maternal electrocardiosignals |
| CN109171708A (en) * | 2018-10-25 | 2019-01-11 | 广东工业大学 | One kind can defibrillation rhythm of the heart identification device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101461711A (en) | Shockable rhythm recognition algorithm based on standard deviation of standard slope absolute value | |
| CN104382590B (en) | What a kind of combination electrocardio time and frequency domain characteristics were analyzed can Electrical Cardioversion rhythm of the heart automatic identification and classifying method | |
| Minami et al. | Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network | |
| Mohanty et al. | Efficient classification of ventricular arrhythmias using feature selection and C4. 5 classifier | |
| Jekova et al. | Real time detection of ventricular fibrillation and tachycardia | |
| CN101461709B (en) | Shockable rhythm recognition instrument | |
| CN101461710B (en) | Shockable rhythm recognition algorithm based on grid projection distribution dispersion | |
| Amann et al. | A new ventricular fibrillation detection algorithm for automated external defibrillators | |
| Gadaleta et al. | A method for ventricular late potentials detection using time-frequency representation and wavelet denoising | |
| US20140207012A1 (en) | Systems and methods for analyzing electrocardiograms to detect ventricular fibrillation | |
| Romero et al. | ECG frequency domain features extraction: A new characteristic for arrhythmias classification | |
| Sadr et al. | A low-complexity algorithm for detection of atrial fibrillation using an ECG | |
| Yu et al. | A switchable scheme for ECG beat classification based on independent component analysis | |
| Hotradat et al. | Empirical mode decomposition based ECG features in classifying and tracking ventricular arrhythmias | |
| Dliou et al. | Abnormal ECG signals analysis using non-parametric time–frequency techniques | |
| Othman et al. | A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation | |
| CN101474069B (en) | Improved shockable cardioversion identifying instrument | |
| Murthy et al. | Ecg signal denoising and ischemic event feature extraction using daubechies wavelets | |
| Watson et al. | Practical issues in the evaluation of methods for the prediction of shock outcome success in out-of-hospital cardiac arrest patients | |
| Rieta et al. | Applications of signal analysis to atrial fibrillation | |
| Khadra et al. | A new quantitative analysis technique for cardiac arrhythmia using bispectrum and bicoherency | |
| Nemirko et al. | The Comparison of Algorithms for Life-threatening Cardiac Arrhythmias Recognition. | |
| Ilankumaran et al. | Ventricular arrhythmias detection using wavelet decomposition | |
| Oster et al. | An artificial model of the electrocardiogram during paroxysmal atrial fibrillation | |
| Dliou et al. | Noised abnormal ECG signal analysis by combining EMD and Choi-Williams techniques |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C12 | Rejection of a patent application after its publication | ||
| RJ01 | Rejection of invention patent application after publication |
Open date: 20090624 |