Key result
Hybrid CNN-LSTM outperforms standalone models for ECG classification with ~83% accuracy.
Why the study?
Does a Hybrid CNN-LSTM approach improve ECG signal classification performance compared to CNN or LSTM alone in PhysioNet ECG recordings?
Does a Hybrid CNN-LSTM approach improve ECG signal classification performance compared to CNN or LSTM alone in PhysioNet ECG recordings?
A hybrid CNN-LSTM deep learning model achieves high accuracy in classifying ECG signals, demonstrating potential for automated diagnosis support.
Hypothesis-generating for automated ECG tools; leaves open prospective clinical validation before practice change.
In this paper, the application of deep learning for ECG signal classification is discussed. Three different approaches, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Hybrid CNN-LSTM, are developed and compared. The ECG dataset from PhysioNet is used for the development of the three approaches. The dataset consists of 162 ECG recordings classified into three diagnostic categories: ARR (arrhythmia), CHF (congestive heart failure), and NSR (normal sinus rhythm). The comparisons are evaluated based on performance metrics such as accuracy, precision, recall, and F1-Score. The results show that the hybrid method achieved the best performance with an accuracy of 82.60% and an F1-Score of 93.75%, combining CNN's strengths in spatial analysis and LSTM's strengths in temporal patterns. However, detecting minority classes such as CHF remains a challenge. This study highlights the potential of deep learning to support efficient and accurate ECG-based diagnoses and emphasizes the need for further optimization to enhance performance.
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Abidin et al. (2025) studied Arrhythmia, congestive heart failure, and normal sinus rhythm (n=162). Hybrid CNN-LSTM approach vs. CNN and LSTM standalone approaches was evaluated on ECG signal classification performance (accuracy and F1-Score). A hybrid CNN-LSTM approach for ECG signal classification achieved the best performance with an accuracy of 82.60% and an F1-Score of 93.75% compared to standalone CNN and LSTM models.
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