Key result
Hybrid CNN-BiGRU model improves inter-patient ECG classification accuracy to ~94% over standalone CNN.
Why the study?
This study aimed to develop and validate a machine learning framework using hybrid deep learning architectures that jointly capture local morphological patterns and sequential dependencies in ECG signals to support rapid arrhythmia detection.
Does a hybrid CNN–BiGRU deep learning model improve automated inter-patient ECG beat classification accuracy and F1-score compared to standalone CNN, CNN–LSTM, and CNN–BiLSTM architectures on combined MIT-BIH and INCART datasets?
Does a hybrid CNN–BiGRU deep learning model improve automated inter-patient ECG beat classification accuracy and F1-score compared to standalone CNN, CNN–LSTM, and CNN–BiLSTM architectures on combined MIT-BIH and INCART datasets?
Combining convolutional neural networks with bidirectional gated recurrent units (CNN–BiGRU) improves morphological and temporal feature representation for automated cross-database ECG arrhythmia beat classification, achieving 93.89% accuracy.
Hypothesis-generating for hybrid DL ECG classification; requires prospective validation before clinical use.
This study aims to develop and validate a robust machine learning-based framework for automated ECG beat classification, with the purpose of supporting rapid arrhythmia detection and improving the efficiency of clinical decision support systems. The work focuses on assessing the effectiveness of hybrid deep learning architectures that can jointly capture local morphological patterns and sequential dependencies in ECG signals. The proposed experimental framework was designed using publicly available MIT-BIH Arrhythmia and AAMICS INCART databases. ECG recordings were pre-processed and augmented through Gaussian noise addition, amplitude reduction, and temporal shifting to improve model generalization under signal variability. Several deep learning models were trained and evaluated, including convolutional and hybrid recurrent architectures, to identify the most effective approach for ECG record analysis. The findings demonstrate that the CNN–BiGRU architecture achieved the best overall performance, with an accuracy of 93.89%, precision of 93.36%, recall of 93.91%, and F1-score of 93.39%. These results indicate that combining convolutional neural networks with bidirectional gated recurrent units improves the representation of both morphological and temporal ECG characteristics. The study provides proof of concept for using hybrid deep learning architectures in automated ECG analysis. The practical implication of this work is that the proposed CNN–BiGRU framework may support faster, more reliable ECG interpretation and has potential applicability in real-time arrhythmia screening, wearable monitoring systems, and clinical decision support environments.
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Kumar et al. (2026) studied Cardiac arrhythmias. CNN-BiGRU hybrid deep learning model vs. Standalone 1D CNN model (as well as CNN-LSTM and CNN-BiLSTM architectures) was evaluated on Macro-averaged overall classification accuracy across five AAMI heartbeat classes (N, S, V, F, Q) on the combined cross-database test set. The hybrid CNN-BiGRU model achieved 93.89% accuracy and a 93.39% F1-score for inter-patient ECG beat classification, outperforming standalone CNN (89.95% accuracy, 87.13% F1-score).
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