Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

Author:

Li DengaoORCID,Wu Hang,Zhao Jumin,Tao Ye,Fu Jian

Abstract

Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.

Publisher

MDPI AG

Subject

Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GCN-ResNet: A Multi-label Classifier for ECG Arrhythmia;12th Asian-Pacific Conference on Medical and Biological Engineering;2024

2. SAR model for accurate detection of multi-label arrhythmias from electrocardiograms;Heliyon;2023-11

3. LSTM-Based Arrhythmia Classification in Electrocardiogram Signals;2023 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob);2023-10-10

4. MUSE: MUlti-lead Sub-beat ECG for remote AI based atrial fibrillation detection;Journal of Network and Computer Applications;2023-03

5. Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review;Diagnostics;2022-12-29

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