Ecg-based arrhythmia classification using discrete wavelet transform and attention-enhanced CNN-BiGRU model

Electrocardiogram (ECG)-based arrhythmia classification is crucial for the early detection and diagnosis of cardiovascular diseases. However, the presence of noise in raw ECG signals presents significant challenges to classification performance. In this study, we propose a novel approach that combines discrete wavelet transform (DWT) for signal denoising with an Attention-Enhanced Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for arrhythmia classification. First, DWT is applied to eliminate noise while preserving essential morphological features of the ECG signals. To address class imbalance, the Borderline-SMOTE algorithm is applied to generate synthetic samples for minority classes. The preprocessed signals are then passed through a CNN for hierarchical feature extraction, followed by a BiGRU to capture temporal dependencies. An attention mechanism is integrated to emphasize the most informative regions of the signal, enhancing the model's discriminative capability. The proposed method was evaluated on the MIT-BIH arrhythmia database and achieved an accuracy of 99.22% across five arrhythmia categories, outperforming several existing methods. This approach provides an effective solution for automatic arrhythmia detection in clinical practice.