System and method for distinguishing a cardiac event from noise in an electrocardiogram (ECG) signal

Patent metadata

Field	Value
Publication number	US-9724008-B2
Application number	US-201514791836-A
Country	US
Kind code	B2
Filing date	Jul 6, 2015
Priority date	Jul 7, 2014
Publication date	Aug 8, 2017
Grant date	Aug 8, 2017

Abstract

A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning.

First claim

The invention claimed is:

1. A cardiac monitoring device comprising:
at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient;
a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and
at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to:
obtain the ECG signal from the at least one sensing electrode;
determine a transformed ECG signal based on the ECG signal;
extract at least one value representing at least one feature of the transformed ECG signal;
provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score;
compare the ECG-derived score to a predetermined threshold score determined by machine learning; and
provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score,
wherein the machine learning is one of a multivariate adaptive regression splines classifier and a neural network classifier.

2. The cardiac monitoring device of claim 1 , wherein the transformed ECG signal comprises a frequency-domain representation of the ECG signal.

3. The cardiac monitoring device of claim 1 , wherein the transformed ECG signal comprises a representation of a power distribution of the ECG signal over a range of frequencies of the ECG signal.

4. The cardiac monitoring device of claim 1 , wherein the transformed ECG signal comprises a power spectral density (PSD) of the ECG signal, the PSD being determined by calculating a fast Fourier transform (FFT) of the ECG signal.

5. The cardiac monitoring device of claim 4 , wherein determining the PSD comprises calculating the fast Fourier transform (FFT) of the ECG signal and performing a square of a modulus of the FFT to transform the FFT into a real number.

6. The cardiac monitoring device of claim 4 , wherein at least four features of the PSD are extracted and provided to the machine learning.

7. The cardiac monitoring device of claim 6 , wherein the at least four features of the PSD that are extracted are: at least one value representing a dominant frequency of the PSD; at least one value representing in-band entropy of the PSD between frequencies of 2 Hz and 6 Hz; at least one value representing first-band entropy of the PSD between frequencies of 0 Hz and 2 Hz; and at least one value representing a variance of the PSD.

8. The cardiac monitoring device of claim 1 , wherein the cardiac monitoring device is one of a wearable defibrillator, an implantable defibrillator, an automated external defibrillator (AED), a mobile cardiac telemetry device, an ECG rhythm classifier, a ventricular arrhythmia detector, and a Holter monitor.

9. The cardiac monitoring device of claim 1 , further comprising:
providing an instruction signal for taking an action based on the indication.

10. The cardiac monitoring device of claim 9 , wherein the action is at least one of applying a therapy to a patient and providing a warning signal to the patient.

11. The cardiac monitoring device of claim 9 , further comprising an alert device operatively coupled to the at least one processor for providing the instruction signal to the patient.

12. The cardiac monitoring device of claim 11 , wherein the alert device is configured to provide the instruction signal as at least one of an audio signal and a visual signal.

13. The cardiac monitoring device of claim 1 , wherein the program instructions that are executed by the at least one processor are initiated for a portion of the ECG signal that is stored in a memory device when the at least one processor detects a triggering event.

14. The cardiac monitoring device of claim 13 , wherein the portion of the ECG signal is a predetermined time period of the ECG signal that precedes the triggering event.

15. The cardiac monitoring device of claim 14 , wherein the predetermined time period is 20 seconds.

16. The cardiac monitoring device of claim 13 , wherein the triggering event is at least one of detection of a ventricular fibrillation (VF) in the ECG signal and detection of a ventricular tachycardia (VT) event in the ECG signal.

17. The cardiac monitoring device of claim 1 , wherein the indication of the cardiac event is provided if the ECG-derived score is one of above or below the predetermined threshold score.

18. A cardiac monitoring device comprising:
at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient;
a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and
at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to:
obtain the ECG signal from the at least one sensing electrode;
determine a transformed ECG signal based on the ECG signal;
extract at least one value representing at least one feature of the transformed ECG signal;
provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score;
compare the ECG-derived score to a predetermined threshold score determined by machine learning; and
provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score,

wherein the machine learning is based on a training data set comprising a collection of ECG signals associated with treatments performed by a plurality of defibrillators.

19. The cardiac monitoring device of claim 18 , wherein the collection of ECG signal includes at least noisy normal sinus rhythm signals and tachyarrhythmia signals.

20. A cardiac monitoring device comprising:
at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient;
a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and
at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to:
obtain the ECG signal from the at least one sensing electrode;
determine a transformed ECG signal based on the ECG signal;
extract at least one value representing at least one feature of the transformed ECG signal;
provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score;
compare the ECG-derived score to a predetermined threshold score determined by machine learning; and
provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score,

wherein the machine learning is based on a training data set comprising a collection of ECG signals stored in a memory of the cardiac monitoring device.

21. A cardiac monitoring device comprising:
at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient;
a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and
at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to:
obtain the ECG signal from the at least one sensing electrode;
determine a transformed ECG

Assignees

• Zoll Medical Corp

Inventors

• Sullivan Adam
• Kaib Thomas E
• Nicolo Francesco
• Szymkiewicz Steve

Classifications

• A61N1/3987

  characterised by the timing or triggering of the shock · CPC title

• A61N1/3925

  Monitoring; Protecting · CPC title

• A61B5/746

  Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms · CPC title

• A61N1/046

  Specially adapted for shock therapy, e.g. defibrillation · CPC title

• A61N1/3956

  Implantable devices for applying electric shocks to the heart, e.g. for cardioversion · CPC title