An Analysis and Identification Method, System and Storage Medium for Electrocardiogram

A method for analyzing an electrocardiogram (ECG) to detect arrhythmia using machine learning techniques, the method comprising:

• training, by one or more processors, a machine learning model to detect arrhythmia using, for a plurality of ECGs for a plurality of patients, (i) encoded electrogram data from the ECG indicating characteristics of each band of a heartbeat, wherein the electrogram data is encoded by clustering each band into a cluster of a plurality of clusters each assigned a cluster number, (ii) an indication of whether the patient has arrhythmia and, for a subset of the plurality of patients that have arrhythmia, (iii) an indication of a type of arrhythmia of a plurality of types of arrhythmia;
• obtaining, by the one or more processors, electrogram data from an ECG for a patient;
• encoding, by the one or more processors, the electrogram data from the ECG for the patient by clustering each band of the electrogram data;
• and
• applying, by the one or more processors, the encoded electrogram data from the ECG for the patient to the trained machine learning model to diagnose a particular type of arrhythmia for the patient.

A computing device for analyzing and identifying electrocardiogram, the computing device comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium storing instruction thereon, that when executed by the one or more processors, cause the computing device to:
• train a machine learning model to detect arrhythmia using, for a plurality of ECGs for a plurality of patients, (i) encoded electrogram data from the ECG indicating characteristics of each band of a heartbeat, wherein the electrogram data is encoded by clustering each band into a cluster of a plurality of clusters each assigned a cluster number, (ii) an indication of whether the patient has arrhythmia and, for a subset of the plurality of patients that have arrhythmia, (iii) an indication of a type of arrhythmia of a plurality of types of arrhythmia;
• obtain electrogram data from an ECG for a patient;
• encode the electrogram data from the ECG for the patient by clustering each band of the electrogram data;
• and
• apply the encoded electrogram data from the ECG for the patient to the trained machine learning model to diagnose a particular type of arrhythmia for the patient.

A non-transitory computer-readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

• train a machine learning model to detect arrhythmia using, for a plurality of ECGs for a plurality of patients, (i) encoded electrogram data from the ECG indicating characteristics of each band of a heartbeat, wherein the electrogram data is encoded by clustering each band into a cluster of a plurality of clusters each assigned a cluster number, (ii) an indication of whether the patient has arrhythmia and, for a subset of the plurality of patients that have arrhythmia, (iii) an indication of a type of arrhythmia of a plurality of types of arrhythmia;
• obtain electrogram data from an ECG for a patient;
• encode the electrogram data from the ECG for the patient by clustering each band of the electrogram data;
• and
• apply the encoded electrogram data from the ECG for the patient to the trained machine learning model to diagnose a particular type of arrhythmia for the patient.