WO2009103108A1 - Diagnostic device and method for detecting an acute coronary episode - Google Patents
Diagnostic device and method for detecting an acute coronary episode Download PDFInfo
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- WO2009103108A1 WO2009103108A1 PCT/AU2009/000178 AU2009000178W WO2009103108A1 WO 2009103108 A1 WO2009103108 A1 WO 2009103108A1 AU 2009000178 W AU2009000178 W AU 2009000178W WO 2009103108 A1 WO2009103108 A1 WO 2009103108A1
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- WIPO (PCT)
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- output signal
- acute coronary
- ppg
- troponin
- patient
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Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
Definitions
- the present invention relates to an apparatus and method for detecting acute coronary syndromes.
- Ischaemic heart diseases is reported to be the leading cause of mortality in Australia. Health expenditure classified by disease or injury group is highest for cardiovascular diseases at an estimated $5.48 billion in 2000-01. According to the WHO, there were 7.1 million deaths from coronary heart disease globally m 1999.
- Acute coronary syndromes form the diagnostic and pathophysiological continuum from unstable angina to myocardial infarction. It is the goal of the emergency physician to accurately diagnose and treat a patient with potentially life- 25 threatening ACS, while avoiding the misdiagnosis and discharge of these patients. Due to the variability of clinical manifestations of ACS between unstable angina and acute myocardial infarction, the diagnosis of ACS can be challenging.
- Rate Variability Standards and Measurement, Physiological Interpretation and Clinical Use. Circulation 1996;93(5):1043-65).
- FDA frequency domain analysis
- the present invention aims to provide a non-invasive, reliable apparatus and method for diagnosing acute coronary syndromes.
- the present invention provides an apparatus for detecting an acute corona ⁇ y episode of an acute coronary syndrome in a patient comprising: a receiving means to receive one or more input signals from a photoplethysmography (PPG) instrument; processing means to process said one or more input signals and provide at least one output signal; output means to present said at least one output signal; characterised in that said at least one output signal is representative of whether or not said patient is experiencing said acute coronary episode.
- PPG photoplethysmography
- the present invention provides an apparatus for detecting an acute coronary episode of an acute coronary syndrome in a patient said apparatus comprising:
- a photoplethysmography unit to receive a photoplethysmography signal from a LED/photodetector unit attached to a patient and to convert said photoplethysmography signal to an input signal; processing means to process said one or more input signals and provide at least one output signal; output means to present said at least one output signal; characterised in that said at least one output signal is representative of whether or not said patient is experiencing said acute coronary episode.
- the present invention provides an apparatus for detecting an acute coronary episode of an acute coronary syndrome in a patient comprising: a receiving means to receive one or more input signals from a photoplethysmography (PPG) instrument and one or more ECG input signals ; processing means to process said input signals and provide at least one output signal; output means to present said at least one output signal; characterised in that said at least one output signal is representative of whether or not said patient is experiencing said acute coronary episode.
- PPG photoplethysmography
- Acute coronary syndromes is a term used to represent the diagnostic and pathophysiological continuum from unstable angina to myocardial infarction. ' •
- the photoplethysmography (PPG) instrument of the first aspect may comprise a pulse oximetry instrument (POI).
- the POI measures light transmission as a function of time and may present a signal indicative of the tissue blood volume. Changes in the blood volume during systole and diastole may therefore be measured.
- the one or more input signals may comprise measurements of the blood volume over a period of time. Typically said one or more input signals comprises a waveform signal comprising a series of systolic peaks and diastolic troughs measured over said time period.
- Either the pulse oximetry instrument- of the first aspect or the pulse oximtery unit of the second aspect comprise or may receive data from an LED/photodetector unit.
- the LED/photodetector unit may comprise two LEDs, a red LED and an infrared LED that alternately illuminate a peripheral blood sample with two wavelengths of electromagnetic radiation.
- the photodetector may convert the incident radiation to a varying electrical signal which may comprise said one or more input signals.
- the one or more input signals from either the pulse oximetry instrument or the pulse oximetry unit are received by said receiving means wherein said receiving means converts the one or more input signals into a modified signal suitable for further processing.
- the processing means is typically a central processing unit (CPU) of a computer system.
- the processing means includes frequency domain analysis software.
- the processing means may also include filtering means to filter the input signal and remove any spikes or trough sequences caused by artefacts.
- the processing means may further comprise an interpolating means.
- the interpolating means interpolates the systolic peak and diastolic trough sequences to evenly spaced samples in time.
- Methods of interpolation may include staircase-type interpolation, linear interpolation or spline interpolation.
- the at least one output signal may include a number of frequency components determined by frequency domain analysis of the processed at least one input signal.
- the frequency components may range between very low frequency oscillations and relatively high frequency oscillations.
- At least one component of the output signal comprises a very low frequency (VLF) component.
- VLF very low frequency
- the frequency range of this component may be between 0.015 Hz and 0.035Hz.
- a further component of the output signal may comprise a first low frequency component (LF).
- LF low frequency component
- the frequency range of this component may be in the order of 0.035Hz to 0.08Hz.
- Another component of the output signal may be a second low frequency component (MF).
- the frequency Tange of this component may he in the order of 0.08Hz to 0.150Hz.
- the output signal may comprise a high frequency (HF) component typically in the range of 0.150Hz to 0.450Hz.
- HF high frequency
- the HF component is considered to relate to respiration and the LF component or MF component that occurs at a lower frequency than the HF component ie less than 0.15Hz relate to the autonomic nervous system.
- the output signal may be presented in a number of forms.
- the output signal may be presented as a spectral plot.
- the plot may depict spectral peaks of some or all of the abovementioned frequency components.
- the output signal may comprise a spectral peak having relatively elevated power spectral density at certain frequencies and relatively low power spectral density at other frequencies.
- the spectral plot may comprise a relatively elevated power spectral density for a low density component ie LF and/or MF. Such elevation may he indicative of an acute coronary episode including myocardial infarction.
- the spectral plot may include a representation of the high frequency component of the output signal that is in the range of 0.150Hz and 0.450Hz wherein the power spectral density of this component is relatively depressed.
- the processing means further processes the signal to a positive or negative format ie a signal for "yes there is an acute coronary episode" or "no, there is no acute coronary episode detected".
- the processing means may process an elevated LF component either alone or in addition to a depressed HF component to a signal that is receivable by the output means said output means presenting said signal as a positive or negative signal.
- the output means may comprise at least one indicia means including a light or a series of lights. ' Alternatively, the output signal maybe processed to an auditory signal.
- the output signal may be processed to a graded signal depending upon the severity of the coronary episode. For example, a mild episode may be presented as a light of one colour, a moderate episode presented as a light of a different colour and a severe episode presented as a light of a still different colour. Similarly, in the embodiment wherein the output signal is processed to an auditory signal, the severity of the episode may be presented by different types or volume of auditory signal.
- the latter embodiment may be useful in determining between various syndromes which make up the spectrum of acute coronary syndromes.
- unstable angina may be detected by the apparatus of the present invention which present a suitable indicator of said syndrome.
- the apparatus may detect a more severe episode such as a myocardial infarction and provide an indicator to this effect.
- the present invention provides a diagnostic apparatus for detecting an acute coronary episode of an acute coronary syndrome in a patient comprising: a receiving means to receive one or more input signals from an ECG; processing means to process said one or more input signals and provide at least one output signal; output means to present said at least one output signal; characterised in that said at least one output signal is representative of heart rate variability (HRV) of the patient and wherein said output signal is indicative of whether or not said patient is experiencing said acute coronary episode.
- HRV heart rate variability
- the present invention provides a system for diagnosing an acute coronary episode of an acute coronary syndrome in a patient said system including: receiving one or more input signals from an ECG; processing said one or more input signals and providing at least one output signal; characterised in that said at least one output signal is representative of heart rate variability (HRV) of the patient and wherein said output signal is indicative of whether or not said patient is experiencing said acute coronary episode -
- HRV heart rate variability
- Figure 1 is a schematic representation of one embodiment of an apparatus of the present invention
- Figure 2 is a schematic representation of a further embodiment of the present invention.
- Figure 3 is a schematic spectral plot representing an output signal indicative of an acute coronary episode
- Figures 4 and 5 show the detection of RR from ECG traces
- Figures 6 provides sample traces of finger PPG waveforms;
- Figure 7 provides sample traces of ear PPG waveforms;
- Figure 8a shows a box and whisker plot and corresponding ROC curve depicting the difference in mean heart rate between negative and positive Troponin 1 ;
- Figure 8b shows a box and whisker plot and corresponding ROC curve depicting the difference in mean heart rate between negative and positive Troponin 2 measurements:
- Figure 9 details normalised LF in Troponin 1 positive and negative and Troponin 2 positive and negative ECG and PPG (ear and finger) analyses;
- Figure 10a depicts box and whisker plot and associated ROC curve to show differences in ear PPG MF% between negative and positive Troponin 1;
- FIG. 10b depicts box and whisker plot and associated ROC curve to show differences in ear PPG MF% between negative and positive Troponin 2;
- Figure 11 depicts box and whisker plot and associated ROC curve to show differences in ear PPG MF/HF between negative and positive Troponin 1 ;
- Figure 12 depicts box and whisker plot and associated ROC curve to show differences in ear PPG MF/HF between negative and positive Troponin 2;
- Figure 13a depicts box and whisker plot and associated ROC curve to show the differences in finger (F) PPG LF% between negative and positive Troponin 1 ;
- Figure 13b depicts box and whisker plot and associated ROC curve to show the differences in finger (F) PPG HF% between negative and positive Troponin 1;
- Figure 14a depicts box and whisker plot and associated ROC curve to show the differences in finger (F) PPG MF/HF% between negative and positive Troponin 1;
- Figure 14b depicts box and whisker plot and associated ROC curve to show the differences in finger (F) PPG MF/HF% between negative and positive Troponin 2;
- Figure 15 depicts differences in cross correlation linking RR + E PPG PK, VOL and TR in the LF region between negative and positive Troponin 1 and Troponin 2;
- Figure 16 depicts ROC curves indicating power of cross correlation linking RR + E PPG PK, VOL and TR in the LF region to discriminate between negative and positive Troponin 1;
- Figure 17 depicts the differences in cross correlation linking RR + E PPG PK, VOL and TR in the MF region between negative and positive Troponin 1 and Troponin
- Figure 18 depicts ROC curves indicating power of cross correlation linking RR
- Figure 19a shows output from ECG analysis both in the time domain and associated frequency spectra in a patient with a negative troponin test on admission and subsequent negative testing for myocardial infarction
- Figure 19b shows output from ECG analysis both in the time domain and associated frequency spectra in a patient with a positive troponin test on admission and subsequent positive testing for myocardial infarction
- Figure 20a shows output from PPG analysis both in the time domain and associated frequency spectra in a patient with a negative troponin test on admission and subsequent negative testing for myocardial infarction
- Figure 20b shows output from PPG analysis variability in the time domain and associated frequency spectra in a patient with a positive troponin test on admission and subsequent positive testing for myocardial infarction.
- the apparatus 10 of the present invention was developed as a result of research into the relationship of data from pulse oximetry and ECG and acute coronary episodes.
- the study involved monitoring both pulse oximtery data (processed in the frequency domain and presented as a signal having various frequency components) and heart rate variability taken from ECG data when a subject was admitted to hospital with chest pain. The data was then compared to troponin levels in the patients studied. Troponin is a key biomarker of cardiac injury.
- Troponin I results come from venous blood samples taken from patients on presentation and a later collection for the 8 hour Troponin.
- Troponin 1 in the present results refers to the initial Troponin and Troponin 2 refers to the 8 hour Troponin.
- An elevated Troponin result was considered to be above O.lng/mL.
- Each patient was connected to the Powerlab 16/30 using three standard ECG electrodes on the chest, and pulse oximeter probes on an earlobe and fingertip, with data collected via a BioAmp and saved onto a laptop computer. Each recording lasted approximately ten minutes, with a minimum duration of five minutes. Patients were encouraged not to move during the recording to minimise movement artefact. Sampling of heart rate and pulse oximetry waveform took place at 200Hz.
- Data were entered in an Excel (Microsoft Corp., Redmond, WA) database for analysis. Data analysis was performed using Analyse-It (Analyse-It Software Ltd, Leeds, UK.) Data are described using descriptive statistics, 95% confidence intervals and p values. P-values ⁇ 0.05 were considered significant. Data are presented as median + interquartile range as the distributions were not symmetrical, and Wilkinson's Signed Rank and Mann Whitney U tests, Kruskal-Wallis ANOVA and Receiver Operating Characteristic curve analysis are used where appropriate. Calculations were made of sensitivity, specificity, positive and negative predictive values using contingency tables.
- the power spectra of RR and PPG features were computed by a 2048-pt Fast Fourier Transform (FFT) of the windowed autocorrelation of RR, based on the Blackman Tukey method.
- the cross-power spectra of RR and PPG features were obtained by a 2048-pt FFT of the windowed cross- correlation between the peak and the pulse volume variabilities, also based on the Blackman Tukey method.
- the coherence-weighted cross-power spectrum was computed by the product of the cross-power spectrum and the coherence function, which aimed at emphasizing the highly correlated frequency components that were believed to represent common physiological mechanisms behind the two variability signals (for example, sympathetic modulation and respiratory fluctuation).
- the power spectra of HRV and PPG features and the coherence-weighted cross- power spectra were divided into a LF band (0.04-0.15 Hz) and a HF band (0.15-0.45
- the LF band is influenced by both sympathetic, and vagal nerve activities, and the HF band reflects vagal modulation to some extent.
- the LF band of PPG waveform reflects mainly sympathetic influences on peripheral vessels, whereas the HF band represents predominantly the mechanical effect of respiration.
- Part of the LF band was defined as the mid frequency (MF) band (0.08-0.15 Hz), which has been regarded as a more specific representation of sympathetic vascular modulation by autonomic mechanisms ,
- the power in each band was calculated by integration of the power spectrum over the specified frequency range.
- the powers in the LF and MF bands were expressed in normalized units (nu) after division by the total power in 0.04-0.45 Hz (excluding the very low frequency (VLF) band at ⁇ 0.04 Hz) then multiplied by 100, and denoted as LF% and MF% for spectral analysis and LFnu and MFnu for cross- spectral analysis.
- troponin 1 • Measured troponin I >O.l ⁇ g L "1 on admission to emergency department - referred to as troponin 1
- troponin 2 • Measured troponin I X).l ⁇ g L "1 at 8 hours from the onset of symptoms - referred to as troponin 2
- VLF PSD of very low frequency component (0.015-0.035Hz)
- the following tables show these comparisons along with cut-off, sensitivity, specificity, positive and negative predictive values, medians, interquartile range (IQR), and 95% confidence intervals (CI).
- Figure 9 depicts the differences in ECG LF between negative and positive troponin I.
- Table 1 Significant results and good discrimination in ROC curve from ECG traces (AUC: area under ROC curve; PPV: positive predictive value; NPV: negative predictive value; bpm: beats per minute; ms: milliseconds).
- Table 2 Comparison of medians, IQR, and 95% CI between negative and positive Troponin results for significant FDA components from ECG traces (bpm: beats per minute; ms: -milliseconds;; -ve: negative; +ve: positive; IQR: interquartile range; CI: confidence interval).
- Table 4 Comparison of medians between negative and positive Troponin results for significant FDA components of peak values in ear PPG (mV: millivolts; -ve: negative; +ve: positive; IQR: interquartile range; CI: confidence interval).
- Table 5 Significant results and good discrimination in ROC curve from peak values in finger PPG traces (AUC: area under ROC curve; PPV: positive predictive value; NPV: negative predictive value; mV: millivolts).
- ROC curve analysis showed that LF% was a good discriminator between positive and negative troponin 1 (AUROC 0.80, 95%CI 0.55 to 1.00), and between positive and negative troponin 2 (AUROC 0.83, 95%CI 0.57 to 1.00).
- ROC curve analysis also showed that HF% was a good discriminator between positive and negative troponin 1 (AUROC 0.80, 95%CI 0.55 to 1.00), and between positive and negative troponin 2 (AUROC 0.83, 95%CI 0.57 to 1.00).
- ROC curve analysis showed that MF / HF was a good discriminator between positive and negative troponin 1 (AUROC 0.83, 95%CI 0.63 to 1.00), and between positive and negative troponin 2 (AUROC 0.81, 95%CI 0.60 to 1.00).
- ROC curve analysis showed that MF% was a good discriminator between 0 positive and negative troponin 1 (AUROC 0.83, 95%CI 0.67 to 1.00), and also between positive and negative troponin 2 (AUROC 0.81, 95%CI 0.60 to 1.00).
- ROC curve analysis showed that MF / HF was an excellent discriminator between positive and negative troponin 1 (AUROC 0.90, 95%CI 0.82 to 0.99), and was 15 a fair discriminator between positive and negative troponin 2 (AUROC 0.78, 95%CI 0.49 to 1.00).
- Cross correlation between HRV and PPG - Low Frequency 0 Figure 15 depicts differences in cross correlation linking RR + E PPG PK, VOL and TR in the LF region between negative and positive troponin 1 and troponin 2.
- Figure 16 depicts ROC curves indicating power of cross correlation Unking RR + E PPG PK, VOL and TR in the LF region to discriminate between negative and 5 positive troponin 1.
- VOL in the LF ⁇ egion (AUROC 0.84, 95%CI 0.67 to 1.00).
- the cross correlation Q linking RR + E PPG TR in the LF region was a fair discriminator with an AUROC of
- Figure 18 depicts ROC curves indicating power of cross correlation linking RRO + E PPG MF% PK, VOL and TR in the MF region to discriminate between negative and positive troponin 1
- F PPG HF% was significantly decreased in troponin 1 and troponin 2 positive patients, and the ratio of MF/HF spectral powers also showed5 statistically significant increases in patients positive for both troponin 1 and troponin 2.
- ROC curve analysis revealed that all these tests were classed as having good discriminatory power to identify troponin and troponin 2 positive patients.
- E PPG analysis showed dissimilarities to F PPG analysis.
- E PPG MF% was ⁇ significantly increased in both troponin 1 and troponin 2 patients, compared with test negative patients. This was also true of the MF / HF ratio which also was significantly increased for both troponin tests.
- ROC curve analysis described E PPG MF% as a good , discriminator between positive and negative tests, and MF / HF ratio as an excellent discriminator for troponin 1 and a fair discriminator for troponin 2.
- MF cross-correlations showed superior results — there were statistically very significant increases in RR - E PPG PK 5 RR - E PPG VOL and RR - PPG TR in the troponin 1 positive groups, and ROC curve analysis gave values of 0.93, 0.91 and 0.91 respectively, all defined as having excellent discriminatory power to identify patients with a positive test.
- Figure 19a shows output from ECG analysis both in the time domain and associated frequency spectra in a patient with a negative troponin test on admission and subsequent negative testing for myocardial infarction. Low amplitude spectral peaks are seen in the LF region and high amplitude peaks are seen in the HF region.
- Figure 19b shows output from ECG analysis both in the time domain and associated frequency spectra in a patient with a positive troponin test on admission and subsequent positive testing for myocardial infa ⁇ ction.
- This patient higher amplitude spectral peaks are seen in the LF region and peaks are seen in the HF region are attenuated or missing.
- Figure 20a both in the time domain and associated frequency spectra in a patient with a negative troponin test on admission and subsequent negative testing fo ⁇ myocardial infarction.
- Low amplitude spectral peaks are seen in the LF region and high amplitude peaks are seen in the HF ⁇ egion.
- Figure 20b shows output from PPG analysis variability in the time domain and associated frequency spectra in a patient with a positive troponin test on admission and subsequent positive testing for myocardial infarction. Ia this patient higher amplitude spectral peaks are seen in the LF region and peaks are seen in the HF region are attenuated or missing
- the Tesults provide a technique to assess interaction between HRV and PPG variability.
- the correlation between simultaneous changes in the frequency domain was computed, correlating LF and MF frequency components in HRV with frequency components in the peak, waveform area and baseline PPG variability. This was reinforced by the addition of a coherence-weighted technique which emphasised the frequencies at which there was correlation to allow the most efficient identification of synchronised change.
- This technique gave results that showed excellent discrimination between patients with positive and negative troponin tests on admission, with a positive RR - E PPG PK correlation suggesting that 93% of patients would have a raised troponin.
- the present invention provides a diagnostic apparatus 10 to receive and process data from either a pulse oximeter unit or an ECG unit.
- the processed data is presented in a form which is representative of whether a patient has suffered or is suffering an acute coronary episode.
- the apparatus 10 comprises a receiving means 11 to receive one or more input signals 12 from a photoplethysmography (PPG) instrument 13.
- a processing means 14 to process said one or more input signals 12 and provide at least one output signal 15 is also provided.
- An output means 16 presents the output signal
- the device 10 may include an in-built photoplethysmography unit 13 a.
- the photoplethysmography (PPG) instrument 13 or unit 13a comprises a pulse oximetry. photodetector 13b.
- the detector 13b receives data from an LED unit 17.
- the LED unit comprises two LEDs, a red LED 18a and an infrared LED 18b that alternately illuminate a peripheral blood sample with two . wavelengths of electromagnetic radiation.
- the photodetector 13a converts the incident radiation to a varying electrical signal comprising the input signals 12.
- the input signals are received by the receiving means 11 which processes the signal into a modified signal 19 suitable for further processing.
- the processing means 14 is a central processing unit (CPU) of a computer system.
- the signal is processed to provide a processed signal 20.
- the processed signal is then converted by output means 16 to an output signal 15.
- the output signal 15 may comprise a spectrum of frequency oscillations determined by frequency domain analysis of the processed input signal.
- the frequency oscillations may range between very low frequency oscillations and relatively high frequency oscillations.
- the spectral plot depicted in Figure 3 is indicative of an acute coronary episode including a myocardial infarction (MI).
- MI myocardial infarction
- the signal may also be converted into a positive or negative signal, for example, a light or sound to indicate the presence of a coronary episode.
Abstract
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EP09713539A EP2254465A1 (en) | 2008-02-20 | 2009-02-19 | Diagnostic device and method for detecting an acute coronary episode |
AU2009217219A AU2009217219A1 (en) | 2008-02-20 | 2009-02-19 | Diagnostic device and method for detecting an acute coronary episode |
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AU2008900823 | 2008-02-20 | ||
AU2008900823A AU2008900823A0 (en) | 2008-02-20 | Diagnostic device and method for detecting an acute coronary episode |
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PCT/AU2009/000178 WO2009103108A1 (en) | 2008-02-20 | 2009-02-19 | Diagnostic device and method for detecting an acute coronary episode |
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018102815A1 (en) * | 2016-12-02 | 2018-06-07 | Thomas Jefferson University | Signal processing method for distinguishing and characterizing high-frequency oscillations |
CN108697335A (en) * | 2016-02-25 | 2018-10-23 | 三星电子株式会社 | The method and apparatus for detecting living tissue using signal analysis |
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US5228449A (en) * | 1991-01-22 | 1993-07-20 | Athanasios G. Christ | System and method for detecting out-of-hospital cardiac emergencies and summoning emergency assistance |
WO2001078597A1 (en) * | 2000-04-19 | 2001-10-25 | Cheetah Medical Inc. | Method and apparatus for monitoring cardiovascular condition |
US20060192667A1 (en) * | 2002-01-24 | 2006-08-31 | Ammar Al-Ali | Arrhythmia alarm processor |
US20070213621A1 (en) * | 2004-11-22 | 2007-09-13 | Widemed Ltd. | Detection of heart failure using a photoplethysmograph |
-
2009
- 2009-02-19 EP EP09713539A patent/EP2254465A1/en not_active Withdrawn
- 2009-02-19 WO PCT/AU2009/000178 patent/WO2009103108A1/en active Application Filing
- 2009-02-19 AU AU2009217219A patent/AU2009217219A1/en not_active Abandoned
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5228449A (en) * | 1991-01-22 | 1993-07-20 | Athanasios G. Christ | System and method for detecting out-of-hospital cardiac emergencies and summoning emergency assistance |
WO2001078597A1 (en) * | 2000-04-19 | 2001-10-25 | Cheetah Medical Inc. | Method and apparatus for monitoring cardiovascular condition |
US20060192667A1 (en) * | 2002-01-24 | 2006-08-31 | Ammar Al-Ali | Arrhythmia alarm processor |
US20070213621A1 (en) * | 2004-11-22 | 2007-09-13 | Widemed Ltd. | Detection of heart failure using a photoplethysmograph |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108697335A (en) * | 2016-02-25 | 2018-10-23 | 三星电子株式会社 | The method and apparatus for detecting living tissue using signal analysis |
CN108697335B (en) * | 2016-02-25 | 2021-07-13 | 三星电子株式会社 | Method and apparatus for detecting living tissue using signal analysis |
WO2018102815A1 (en) * | 2016-12-02 | 2018-06-07 | Thomas Jefferson University | Signal processing method for distinguishing and characterizing high-frequency oscillations |
US11510606B2 (en) | 2016-12-02 | 2022-11-29 | Thomas Jefferson University | Signal processing method for distinguishing and characterizing high-frequency oscillations |
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EP2254465A1 (en) | 2010-12-01 |
AU2009217219A1 (en) | 2009-08-27 |
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