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.
Highlights
Electrocardiograms (ECG), as a technical means to record the changes of electrical activity generated by each cardiac cycle, have made outstanding contributions in clinical medicine in the past, especially in the diagnosis of arrhythmia and myocardial infarction [1,2]
Our work aims to establish an automatic classification system for arrhythmia from more heartbeats, more signal leads, and more arrhythmia categories, all while trying to explain the working mechanism of the proposed model from the perspective of a heat map
There was a serious data imbalance, which can be reflected in the overall distribution of arrhythmias of nine categories to some extent
Summary
Electrocardiograms (ECG), as a technical means to record the changes of electrical activity generated by each cardiac cycle, have made outstanding contributions in clinical medicine in the past, especially in the diagnosis of arrhythmia and myocardial infarction [1,2]. It is difficult for doctors to make efficient and accurate diagnoses in the face of tens of thousands of ECG records from different individuals. As the most commonly used auxiliary diagnostic method of heart disease, ECG contains abundant cardiac beat information and clinical features. Classification of arrhythmias based on ECG signals is of great significance for effective diagnosis, treatment, and early warning of various cardiovascular diseases. Most classical ECG classification methods are based on single-lead methods, which are Symmetry 2020, 12, 1827; doi:10.3390/sym12111827 www.mdpi.com/journal/symmetry
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