An enhanced hybrid deep learning model integrating CNN, CBAM, and BiLSTM achieved 99.20% accuracy, 97.50% sensitivity, and 99.81% specificity for ECG heartbeat classification.
Does an enhanced hybrid model combining CNN, BiLSTM, and attention mechanism improve ECG segment classification accuracy?
A hybrid deep learning model combining CNN, CBAM, and BiLSTM achieves high accuracy in classifying ECG segments, potentially alleviating clinician workload.
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 F 1 score. Deep learning methods have great potential to alleviate clinicians’ workload and improve diagnostic accuracy of cardiac diseases.
Najia et al. (Sun,) conducted a other in Cardiac rhythm diseases. Enhanced Hybrid Model Combining CNN, BiLSTM, and Attention Mechanism was evaluated on ECG classification accuracy. An enhanced hybrid deep learning model integrating CNN, CBAM, and BiLSTM achieved 99.20% accuracy, 97.50% sensitivity, and 99.81% specificity for ECG heartbeat classification.
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