Abstract
Electrocardiogram (ECG) is an efficient and commonly used tool for detecting arrhythmias. With the development of dynamic ECG monitoring, an effective and simple algorithm is needed to deal with large quantities of ECG data. In this study, we proposed a method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks. For a short-time (10 s) single-lead ECG signal, the time-frequency distribution matrix of the signal was first obtained using a time-frequency transform method, and then a convolutional neural network was used to discriminate the rhythm of the signal. ECG data in multiple databases were used and were divided into 12 classes. Finally, the performance of three kinds of time-frequency transform methods are evaluated, including short-time Fourier transform (STFT), continuous wavelet transform (CWT), and pseudo Wigner-Ville distribution (PWVD). The best result was obtained by STFT, with an accuracy of 96.65%, an average sensitivity of 96.47%, an average specificity of 99.68%, and an average F1 score of 96.27%, respectively. Especially, the area under curve (AUC) value is 0.9987. The proposed method in this work may be efficient and valuable to detect multiple arrhythmias for dynamic ECG monitoring.
Highlights
Cardiovascular disease is the leading cause of mortality
Inspired by some studies using time-frequency analysis and convolutional neural network (CNN) to detect atrial fibrillation (AF) [25], [26] or to classify different ECG beats [24], this study proposed a method to detect multiple arrhythmias based on time-frequency analysis and CNNs, and has achieved excellent performance on public databases
COMPARISONS BETWEEN TRADITIONAL FEATURES AND DEEP FEATURES In a large number of previous studies, the traditional method of classifying ECG signals often relied on the selection and calculation of various characteristics, which are dependent on the extraction of ECG features, such as the location of
Summary
Cardiovascular disease is the leading cause of mortality. About 17 million people die of cardiovascular disease each year, accounting for 37% of the global death toll. The mortality of cardiovascular disease is accounting for 3/4 of the total number of deaths [1]. The incidence of cardiovascular disease will further increase and this will become a serious public health problem as the population ages and the causes of cardiovascular disease increase, such as obesity, stress [2]. Arrhythmia, which is mainly caused by the abnormal electrical activity of the heart, is one of the main manifestations of cardiovascular disease [3]. As a direct reflect of cardiac electrical activity, The associate editor coordinating the review of this manuscript and approving it for publication was Yongtao Hao
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