System and Method for Distinguishing a Cardiac Event From Noise in an Electrocardiogram (ECG) Signal

Patent metadata

Field	Value
Publication number	US-2016000349-A1
Application number	US-201514791836-A
Country	US
Kind code	A1
Filing date	Jul 6, 2015
Priority date	Jul 7, 2014
Publication date	Jan 7, 2016
Grant date	—

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 if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning.

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 at least four features of the PSD are extracted and provided to the machine learning.

6 . The cardiac monitoring device of claim 5 , 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.

7 . 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.

8 . The cardiac monitoring device of claim 1 , wherein the machine learning is one of a multivariate adaptive regression splines classifier and a neural network classifier.

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 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.

12 . The cardiac monitoring device of claim 11 , wherein the portion of the ECG signal is a predetermined time period of the ECG signal that precedes the triggering event.

13 . The cardiac monitoring device of claim 12 , wherein the predetermined time period is 20 seconds.

14 . A wearable defibrillator comprising:
at least one therapy pad for rendering treatment to a patient wearing the wearable defibrillator; 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 therapy pad and 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 processing unit to:
obtain the ECG signal;
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.

15 . The wearable defibrillator of claim 14 , wherein the transformed ECG signal comprises a frequency-domain representation of the ECG signal.

16 . The wearable defibrillator of claim 14 , 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.

17 . The wearable defibrillator of claim 14 , 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.

18 . The wearable defibrillator of claim 17 , wherein at least four features of the PSD are extracted and provided to the machine learning.

19 . The wearable defibrillator of claim 18 , 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.

20 . The wearable defibrillator of claim 14 , wherein the machine learning is one of a multivariate adaptive regression splines classifier and a neural network classifier.

21 . The wearable defibrillator of claim 14 , further comprising at least one of a display and a speaker operatively connected to the at least one processor for conveying an alert signal to the patient.

22 . The wearable defibrillator of claim 14 , further comprising at least one response mechanism operatively connected to the at least one processor,
wherein the wearable defibrillator is configured to prevent rendering treatment to the patient wearing the wearable defibrillator in response to a patient actuation of the at least one response mechanism.

23 . The wearable defibrillator of claim 14 , further comprising:
providing an instruction signal for taking an action based on the indication.

24 . The wearable defibrillator of claim 23 , wherein the action is at least one of applying a therapy to a patient and providing a warning signal to the patient.

25 . The wearable defibrillator of claim 14 , 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.

26 . The wearable defibrillator of claim 25 , wherein the portion of the ECG signal is a predetermined time period of the ECG signal that precedes the triggering event.

27 . The wearable defibrillator of claim 26 , wherein

Assignees

• Zoll Medical Corp

Inventors

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

Classifications

• A61B5/7221

  Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217) · CPC title

• A61B5/0022

  Monitoring a patient using a global network, e.g. telephone networks, internet · CPC title

• A61N1/3987

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

• A61N1/3925

  Monitoring; Protecting · CPC title

• A61N1/3956

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