Application US20200015694 AI Biotech/Diagnostics: General Diagnostics

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Application US20200015694

Published 2020-01-16

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.

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5 Independent Claims

1-4. (canceled)
5. A computerized-method for classification of an electrocardiogram (ECG) signal obtained from a patient using a neural network, the method comprising: training the neural network with a dataset of pre-characterized ECG signals to generate a trained neural network; receiving the ECG signal sampled at a plurality of time points; analyzing the ECG signal at the plurality of time points using the trained neural network; computing scores based on the analyzed ECG signal to detect anomalies; and assigning labels to the ECG signal based on the scores related to the detected anomalies.
9-12. (canceled)
20. A system for classification of an electrocardiogram (ECG) signal obtained from a patient using a neural network, the system comprising at least one server to: train the neural network with a dataset of pre-characterized ECG signals to generate a trained neural network; receive the ECG signal sampled at a plurality of time points; analyze the ECG signal at the plurality of time points using the trained neural network; compute scores based on the analyzed ECG signal to detect anomalies; and assign labels to the ECG signal based on the scores related to the detected anomalies.
31. A programmed routine comprising instructions that, when executed by at least one processor, cause the at least one processor to: train the neural network with a dataset of pre-characterized ECG signals to generate a trained neural network; receive the ECG signal sampled at a plurality of time points; analyze the ECG signal at the plurality of time points using the trained neural network; compute scores based on the analyzed ECG signal to detect anomalies; and assign labels to the ECG signal based on the scores related to the detected anomalies.