Key result
Patient-adaptive CNN-LSTM model achieves ~99% accuracy for detecting ventricular and supraventricular arrhythmias.
Why the study?
Precise and reliable identification of cardiac arrhythmias from ECG signals is critical for informed clinical decisions, motivating development of patient-adaptive and interpretable detection methods.
A novel hybrid CNN-LSTM deep learning model demonstrates high accuracy (>99%) for ECG arrhythmia detection and generalizes well to external datasets, highlighting its potential for continuous ECG monitoring.
Answers are grounded in this paper and cite their sources.
May support automated arrhythmia detection in continuous monitoring; leaves open prospective clinical validation before routine use.
A precise and reliable identification of cardiac arrhythmias from electrocardiogram (ECG) signals is critical for making informed clinical decisions. In this paper, a patient-adaptive and clinically interpretable a hybrid CNN-LSTM model with an attention mechanism is offered as a framework for ECG arrhythmia identification. The proposed framework combines signal preprocessing techniques with domain knowledge-driven morphological and time-frequency features to jointly analyze the waveform patterns and temporal dependencies in noisy ECG signals. In addition to beat-level classification, a composite arrhythmia burden score is also proposed to offer a patient-level summary of arrhythmic activity, and a lightweight patient-specific adaptation method is used to enhance reliability with limited calibration data. Over 99% accuracy and strong sensitivity to ventricular and supraventricular arrhythmias are demonstrated by experimental results employing a rigorous inter-patient methodology on the MIT-BIH Arrhythmia Database. The suggested framework’s capacity to generalize on actual clinical datasets is further validated by external validation on the PTB-XL dataset without retraining. The proposed framework is also scalable for cloud-based deployment to enable continuous ECG monitoring, thus emphasizing its potential for practical and patient-centric cardiac care.
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Bhagat et al. (2026) studied Cardiac arrhythmias. Hybrid CNN-LSTM model with an attention mechanism was evaluated on Accuracy for arrhythmia identification. A patient-adaptive hybrid CNN-LSTM model with an attention mechanism achieved over 99% accuracy for detecting ventricular and supraventricular arrhythmias on the MIT-BIH Arrhythmia Database.
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