An automatic residual-constrained and clustering-boosting architecture for differentiated heartbeat classification. (August 2022)

• Record Type: Journal Article
• Authors: Hu, Shuaicong; Cai, Wenjie; Gao, Tijie; Wang, Mingjie
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Electrocardiogram (ECG) -- Heartbeat classification -- Residual-constrained -- Clustering-boosting -- Convolutional neural network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103690
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Objective: This paper proposes a residual-constrained and clustering-boosting architecture for automatic patient-specific heartbeat classification. Methods: Heartbeats are segmented from long-term ECG recordings with a customized method to make the heartbeats selectively focus on rhythm information and increase the inter-class difference. The Residual-Squeeze-and-Excitation (Res-SE) block is used in the proposed network to reduce the loss of shallow features and emphasize the important features. The center loss function is applied to constrain the output feature vectors and increase the intra-class compactness of the learned features. Then the output features are concatenated with RR-interval features to calculate the classification results. Finally, the classifier predictions are fine-tuned with clustering-boosting strategy. Results: This architecture is verified on MIT-BIH Arrhythmia database (MIT-DB) and MIT Supraventricular Arrhythmia database (MIT-SVDB). Our method attains F1 scores of 0.96 and 0.90 for ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB) respectively, and has good generalization ability. Conclusion: The proposed method, combining supervised learning with unsupervised learning, provides a new architecture for heartbeat classification. It has shown excellent performance on multi-class heartbeat classification. Significance: The proposed method has the potential to be applied for clinical ECG diagnosis.