Key result
Hybrid CNN–BiLSTM–Fuzzy Logic model detects AF with ~99% accuracy, outperforming standard deep-learning architectures.
Why the study?
Identifying atrial fibrillation from electrocardiogram recordings remains challenging due to complex morphological patterns and temporal variations, while conventional classification approaches provide limited information for interpretable risk assessment.
Does an intelligent hybrid CNN–Bidirectional-LSTM–Fuzzy Logic framework improve automated atrial fibrillation detection accuracy and risk stratification compared to individual CNN, BiLSTM, and combined CNN-BiLSTM models in ECG recordings from the MIT-BIH Atrial Fibrillation Database?
Does an intelligent hybrid CNN–Bidirectional-LSTM–Fuzzy Logic framework improve automated atrial fibrillation detection accuracy and risk stratification compared to individual CNN, BiLSTM, and combined CNN-BiLSTM models in ECG recordings from the MIT-BIH Atrial Fibrillation Database?
Integrating convolutional neural networks, bidirectional LSTM networks, and fuzzy logic inference achieves 99.0% accuracy in automated ECG-based atrial fibrillation detection while providing clinically interpretable risk stratification.
Answers are grounded in this paper and cite their sources.
Should not yet change AF detection practice; leaves open prospective clinical validation of hybrid models.
Atrial fibrillation is a common cardiac rhythm disorder that is associated with serious complications such as stroke, heart failure, and increased cardiovascular risk. Accurate and timely identification of atrial fibrillation from electrocardiogram recordings remains challenging because cardiac signals contain complex morphological patterns and temporal variations, while conventional classification approaches generally provide limited information for interpretable risk assessment. This study aims to develop an intelligent hybrid framework for automated atrial fibrillation detection and risk stratification using electrocardiogram signals. The proposed framework integrates a convolutional neural network for automatic extraction of discriminative morphological characteristics, a bidirectional long short-term memory network for learning temporal dependencies across cardiac signal sequences, and a fuzzy logic inference system for interpretable risk assessment. The fuzzy module combines the learned model information with physiological indicators to categorize the predicted outcomes into clinically interpretable risk levels. Experimental evaluation demonstrated that the proposed framework achieved an overall accuracy of 99.0%, with 98.7% precision, 98.8% recall, and 98.7% F1-score. The proposed framework also demonstrated improved performance compared with the individual convolutional neural network and bidirectional long short-term memory models, which achieved accuracies of 95.8% and 96.9%, respectively, while their combined model achieved 98.1%. The results indicate that integrating morphological feature learning, temporal sequence modelling, and fuzzy reasoning can improve atrial fibrillation classification while providing an interpretable risk-oriented output. The proposed framework therefore shows potential as an intelligent approach for automated electrocardiogram analysis and clinical decision support.
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Mahalakshmi Hemashree R (2026) studied Atrial fibrillation. Hybrid CNN–Bidirectional-LSTM–Fuzzy Logic framework vs. Individual CNN, individual BiLSTM, and combined CNN–BiLSTM models was evaluated on Overall classification accuracy for automated atrial fibrillation detection. The hybrid CNN–BiLSTM–Fuzzy Logic framework achieved 99.0% accuracy for atrial fibrillation detection, outperforming CNN, BiLSTM, and CNN–BiLSTM models.
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