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 fine-grained features and global long-range contextual information from ECG sequences. The ACA module dynamically modulates channel-wise feature weights to emphasize clinically discriminative features while suppressing irrelevant components. On the two benchmark datasets, the proposed model yields macro-average accuracies of 93.97% and 98.79%, alongside overall accuracies of 98.88% and 99.47%.
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