Automatic electrocardiogram classification method, system and device based on deep learning

Abstract

The invention discloses an automatic electrocardiogram classification method, system and device based on deep learning. The method comprises the following steps: dividing acquired labeled original electrocardiogram data into a training set and a verification set according to a preset proportion; constructing a convolutional neural network with residual connection, and substituting the convolutional neural network into the training set and the verification set for training and verification to obtain a trained convolutional neural network; evaluating the trained convolutional neural network by using the labeled test set, and acquiring an automatic electrocardiogram classification model passing the test in combination with evaluation indexes; and inputting an electrocardiogram to be tested into the automatic electrocardiogram classification model to obtain an electrocardiogram classification result. The automatic electrocardiogram classification method based on deep learning can carry outcomprehensive feature extraction, complete judgment task of multi-label classification, comprehensively extract information in the electrocardiogram, and complete classification.

Description

technical field [0001] The invention belongs to the field of electrocardiogram classification methods, and more specifically relates to an automatic electrocardiogram classification method, system and equipment based on deep learning. Background technique [0002] The electrocardiogram is an essential tool for monitoring a patient's cardiac health and is the basis for physicians to make clinical decisions about patient therapy. It is an electrical activity that can indicate cardiac abnormalities including various types of arrhythmias, acute myocardial infarction, and other ion channel diseases. In the clinical treatment of heart and cardiovascular diseases, physicians often use the patient's ECG test results as the first useful information for diagnosing cardiovascular diseases, assisting in judging the patient's heart and cardiovascular health status and disease types, and predicting the patient's possible health risk. [0003] Therefore, timely and accurate ECG classific...

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Problems solved by technology

[0005] In view of the defects of the prior art, the purpose of the present invention is to provide a deep learning-based automatic ECG classification method, system and equipment, aiming to solve the problems of poor stability of the existing computer-aided ECG classification system and only single-label classification

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