Key result
Novel CNN-BiLSTM deep learning model achieves ~97% accuracy in automated ECG arrhythmia classification.
Why the study?
Accurate automated arrhythmia classification from ECG signals remains challenging because of complex temporal dependencies in ECG waveforms and severe class imbalance, with clinically significant arrhythmias being underrepresented.
Does an Attention-Enhanced CNN-BiLSTM framework with confidence-gated inference improve automated arrhythmia classification accuracy and minority-class macro-F1 score in ECG recordings from the MIT-BIH Arrhythmia Database?
Does an Attention-Enhanced CNN-BiLSTM framework with confidence-gated inference improve automated arrhythmia classification accuracy and minority-class macro-F1 score in ECG recordings from the MIT-BIH Arrhythmia Database?
Answers are grounded in this paper and cite their sources.
Integrating a 1D-CNN-BiLSTM architecture with temporal attention, focal loss, and confidence-gated inference enables highly accurate automated ECG arrhythmia detection (96.74% accuracy, 86.54% macro-F1) while improving minority-class recognition on imbalanced datasets.
Automated arrhythmia detection from electrocardiogram (ECG) signals is essential for early diagnosis and continuous cardiac monitoring. However, accurate classification remains challenging due to complex temporal dependencies in ECG waveforms and severe class imbalance, where clinically significant arrhythmias are underrepresented. To address these challenges, we propose an Attention-Enhanced CNN–BiLSTM framework with a Confidence-Gated Inference mechanism for ECG-based arrhythmia detection. The proposed model integrates a one-dimensional convolutional neural network (1D-CNN) to capture local morphological features with a Bidirectional Long Short-Term Memory (BiLSTM) network to model long-range temporal dependencies in ECG signals. A temporal attention mechanism is employed to focus on diagnostically relevant segments of the signal. Additionally, a confidence-gated inference strategy is introduced to improve prediction reliability, particularly for minority classes. An extensive ablation study across 18 configurations is conducted to evaluate the contribution of temporal modeling, attention, and inference strategies. On the MIT-BIH Arrhythmia Database, the proposed model achieves an accuracy of 96.58% and a macro-F1 score of 86.05%. To ensure an unbiased evaluation free of test-set leakage, we further adopt a strict protocol in which all hyperparameters ( γ , τ ) are selected exclusively on a held-out validation set; under this leakage-free protocol the model attains an accuracy of 96.74% and a macro-F1 score of 86.54% on the untouched test set. These findings demonstrate that integrating attention-driven temporal modeling with calibrated inference yields efficient arrhythmia classification on the MIT-BIH benchmark.
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Sharma et al. (2026) studied Cardiac arrhythmia (n=47). Attention-Enhanced 1D-CNN-BiLSTM framework with Confidence-Gated Inference vs. Standard argmax decoding and ablation configurations across 18 model setups was evaluated on Arrhythmia classification accuracy and macro-F1 score under leakage-free evaluation protocol. The Attention-Enhanced CNN-BiLSTM model with confidence-gated inference achieved 96.74% accuracy and an 86.54% macro-F1 score on the MIT-BIH Arrhythmia test set under a leakage-free protocol.
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