Key result
Novel CNN model optimizes classification of sinus rhythm, VT, and VF on surface ECGs.
Why the study?
Diagnosis technology based on ECG signals for cardiovascular diseases remains a research hotspot, with a need to optimize arrhythmia classification methods.
Can a Convolutional Neural Network accurately classify sinus rhythm, ventricular tachycardia, and ventricular fibrillation from ECG signals?
Can a Convolutional Neural Network accurately classify sinus rhythm, ventricular tachycardia, and ventricular fibrillation from ECG signals?
Convolutional Neural Networks with optimized layer structures and time intervals can be used to classify ECG signals for arrhythmia detection.
May support CNN-based ECG classification; leaves open prospective clinical validation before practice change.
Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars? attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.
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Dai et al. (2020) studied Cardiac arrhythmia. Convolutional Neural Network (CNN) vs. Different convolution layer structures and time intervals was evaluated on ECG signal classification (sinus rhythm, Ventricular Tachycardia, and Ventricular Fibrillation). A Convolutional Neural Network utilizing different convolution layer structures and time intervals was developed to optimize the classification of ECG signals into sinus rhythm, VT, and VF.
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