An Enhanced Hybrid Model Combining CNN, BiLSTM, and Attention Mechanism for ECG Segment Classification

Deep learning models are necessary in the field of healthcare for the diagnosis of cardiac rhythm diseases since the conventional ECG classification is based on hand-crafted feature engineering and traditional machine learning. Nevertheless, CNN and BiLSTM architectures provide automatic feature learning, enhancing ECG classification accuracy. The current research work puts forward a framework integrating CNN with CBAM and BiLSTM layers for the purpose of extracting valuable features and classifying ECG signals. The model classifies heartbeats according to the AAMI EC57 standard into 5 categories: normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). To tackle uneven class distributions, SMOTE synthesizes new samples, making the model more robust. Evaluation on MIT-BIH arrhythmia database yields remarkable results with 99.20% accuracy, 97.50% sensitivity, 99.81% specificity, and 98.29% mean F1 score. Deep learning methods have great potential to alleviate clinicians' workload and improve diagnostic accuracy of cardiac diseases.