The DCETEN model provides highly accurate ECG classification with low computational demands, making it suitable for resource-constrained IoT edge devices in smart healthcare.
Currently, Cardiovascular Disease (CVD) remains a significant contributor to premature mortality and escalating health care expenses. Early and accurate detection is crucial for the successful treatment, intervention, and monitoring of heart health. Electrocardiograms (ECGs) are essential for diagnosing and monitoring cardiovascular diseases. However, the increasing demand for ECG signal detection, coupled with a shortage of specialized ECG doctors, has made automatic classification and diagnosis of ECG signals a prominent research area. Traditional ECG signal classification models often involve numerous parameters, rendering them unsuitable for resource-limited IoT devices in smart healthcare scenarios. In response, this paper proposes a novel lightweight ECG signal classification network based on the Transformer model, named DCETEN. Specifically, we introduce a lightweight Efficient Channel Attention (ECA) module, integrating it with Depthwise Separable Convolution (DSC) to design a One-dimensional Convolutional Neural Network (1D-CNN) that enhances feature extraction capabilities. Additionally, we fuse hand-crafted RR interval features and features learned by the Transformer to comprehensively capture the ECG signal characteristics. Finally, to make the proposed method suitable for resource-constrained IoT-based edge devices, we employ pruning techniques to reduce the number of model parameters. We validated the proposed model on the MIT-BIH Arrhythmia Database, achieving 99.84% accuracy and a 99.67% F1 score with low computational and memory requirements, making it suitable for deployment in smart healthcare settings with prevalent resource limitations.
Jiang et al. (Fri,) studied this question.