Patent US10426364

Automatic method to delineate or categorize an electrocardiogram
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US10426364

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: (4) (4)

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. (0)

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. (0)

4. The method according to claim 1, further comprising displaying labels with the ECG signal. (0)

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. (2) (0)

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: (4) (4)

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. (0)

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. (0)

11. The programmed routine of claim 8, further comprising a display routine that displays labels with the ECG signal. (0)

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. (2) (0)

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Patent US10426364

Automatic method to delineate or categorize an electrocardiogram

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