Automatic method to delineate or categorize an electrocardiogram
A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.
1. A method for computerizing delineation and multi-label classification of an ECG signal, the ECG signal represented by a multiplicity of ECG data points, the method comprising applying a convolutional neural network to the ECG signal, wherein the convolutional neural network:
reads each one of the multiplicity of ECG data points;
analyzes temporally each one of the multiplicity of ECG data points, each one of the multiplicity of ECG data points corresponding to a time point;
assigns to each one of the multiplicity of EGG data points a score for at least two of a P-wave, a QRS complex, a T-wave, or no wave; and
allocates to each time point an absence, a single, or a multiplicity of corresponding waves based on the scores assigned to each one of the multiplicity of ECG data points.
2. The method according to claim 1 , further comprising, prior to applying the convolutional neural network, denoising and removing a baseline of the ECG signal.
3. The method according to claim 1 , further comprising, after applying the convolutional neural network, determining a beginning and an end for one or more waves that comprise the ECG signal.
4. The method according to claim 1 , further comprising displaying labels with the ECG signal.
5. The method according to claim 1 , wherein the convolutional neural network further detects one or more anomalies, and computes a score for each of the one or more anomalies.
6. The method according to claim 5 , further comprising displaying a list of the one or more detected anomalies.
7. The method according claim 5 , further comprising generating at least one label corresponding to the score for each of the one or more anomalies.
8. A programmed routine for use with a computer for delineating and classifying an ECG signal, the ECG signal represented by a multiplicity of ECG data points, the programmed routine comprising a convolutional neural network that:
reads each one of the multiplicity of ECG data points;
analyzes temporally each one of the multiplicity of ECG data points, each one of the multiplicity of ECG data points corresponding to a time point;
assigns to each one of the multiplicity of ECG data points a score for at least two of a P-wave, a QRS complex, a T-wave, or no wave; and
allocates to each time point an absence, a single, or a multiplicity of corresponding waves based on the scores assigned to each one of the multiplicity of ECG data points.
9. The programmed routine of claim 8 , further comprising, prior to applying the convolutional neural network, denoising and removing a baseline of the ECG signal.
10. The programmed routine of claim 8 , further comprising, after applying the convolutional neural network, determining a beginning and an end for one or more waves that comprise the ECG signal.
11. The programmed routine of claim 8 , further comprising a display routine that displays labels with the ECG signal.
12. The programmed routine of claim 8 , wherein the programmed routine, comprising the convolutional neural network further detects one or more anomalies, and computes a score for each of the one or more anomalies.
13. The programmed routine of claim 12 , further comprising a display routine that displays a list of the one or more detected anomalies.
14. The programmed routine of claim 12 , further comprising a label routine that generates at least one label corresponding to the score for each of the one or more anomalies.