Abstract
Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.
Highlights
Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients
Considering the monitoring time, ECG monitoring can be divided into long-term monitoring and short-term monitoring [5]. e dynamic ECG belongs to long-term monitoring, while the static ECG and the ECG obtained through the wearable ECG acquisition device are short-term monitoring
Experiments and Results e data used in this work were obtained from a clinical 12lead multilabel Cardiovascular Disease Database (CCDD), including nine categories of ECG signals: (1) sinus arrhythmia (SA); (2) sinus bradycardia (SB); (3) T wave low and flat (TWLF); (4) sinus tachycardia (ST); (5) complete right bundle branch block (CRBBB); (6) atrial fibrillation (AF); (7) atrial premature beat (APB); (8) first-degree atrioventricular block (I-AVB); (9) premature ventricular contraction (PVC)
Summary
A detailed review of the patient’s ECG by the clinician, mainly looking at rhythm and waveform abnormalities, requires extensive experience and medical theory, but it is time-consuming and laborious to produce a clinically experienced cardiologist. Awni et al [4] proposed the use of an end-to-end deep neural network (DNN) to classify 12 ECG categories from single-lead electrocardiographic signals, with the mean F1 score for DNN of 0.837 exceeding the mean of 0.780 by general cardiologists. Ullah et al [20] changed the one-dimensional time series into two-dimensional spectra by short-time Fourier transform, and the proposed deep neural network model was a two-dimensional CNN composed of four convolution layers and four pooling layers to classify eight categories of the ECG in MITBIH arrhythmia database with the accuracy of 99.11%. Li et al [21] used the BiLSTM-attention model to perform five different categories of ECG classification on the MIT-BIH
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