MSCA-TNet based deep learning method for ECG arrhythmia classification

作者：Huang S, Chen S, Guo Z, Ji K, Zhang M · 发表于：Scientific reports · 年份：2026 · DOI：10.1038/s41598-026-58850-y

Electrocardiography (ECG) is a widely adopted modality for monitoring cardiac rate and rhythm and identifying various abnormalities of the cardiac electrical system. Research on ECG preprocessing and intelligent classification is vital for the early diagnosis and clinical management of cardiovascular diseases and helps improve the accuracy and efficiency of cardiac diagnosis. Nevertheless, ECG signals captured by wearable acquisition devices frequently suffer from poor signal quality due to subject motion and uncontrolled acquisition environments. Furthermore, native ECG waveforms are faint and susceptible to diverse noise interference. To diagnose noise-corrupted arrhythmia, clinicians need to inspect lengthy ECG recordings relying on professional expertise, which is tedious, labor-intensive and prone to subjective bias. Another critical challenge lies in severe class imbalance within public ECG datasets, resulting in degraded classification performance for minority arrhythmia categories. Accordingly, developing robust automated arrhythmia detection algorithms is essential to ease the diagnostic workload of clinical practitioners. In this work, discrete wavelet transform (DWT) is adopted for noise elimination, and the SMOTE-Tomek hybrid resampling strategy is applied to mitigate class imbalance on the MIT-BIH and INCART datasets. We further propose MSCA-TNet, a hybrid architecture embedding the adaptive channel attention (ACA) module and Transformer to extract both local fin...