Key result
NeuroCardioSenseNet-Fusion improves inter-patient arrhythmia classification accuracy to ~91% over existing methods.
Why the study?
Existing deep learning methods for ECG classification struggle to jointly characterize morphological patterns, multi-lead interactions, and temporal dependencies.
Does the NeuroCardioSense framework improve the accuracy of multi-lead ECG classification and arrhythmia detection compared to existing baseline methods?
Does the NeuroCardioSense framework improve the accuracy of multi-lead ECG classification and arrhythmia detection compared to existing baseline methods?
The novel NeuroCardioSense deep learning framework demonstrates high intra- and inter-patient accuracy for automated arrhythmia detection on the MIT-BIH database.
May enhance automated arrhythmia detection in clinical ECG monitoring; extends deep learning models but requires prospective validation before adoption.
Accurate classification of electrocardiogram (ECG) signals is essential for automated arrhythmia detection and clinical decision support. Existing deep learning methods still struggle to jointly characterize morphological patterns, multi-lead interactions, and temporal dependencies, leading to limited representation of waveform details, rhythm dynamics, and class boundary separability. To address these challenges, we propose a NeuroCardioSense (NCS) framework built upon a convolutional neural network (CNN) backbone, comprising a base model, NeuroCardioSenseNet (NCSN), and an enhanced variant, NeuroCardioSenseNet-Fusion (NCSNF). NCSN constructs a novel Time-Aware Gated Convolution (TAG-Conv) layer together with a Time-Aware Gating Mechanism (TAGM), which adaptively modulate convolutional filters and cross-lead feature contributions based on local temporal context and channel energy distributions. This design enables joint modeling of morphological features and short-range temporal dynamics while reinforcing inter-lead coherence. Building upon NCSN, NCSNF incorporates a Time-Fuzzy Integration Module (TFIM) that constructs a learnable fuzzy subspace by jointly encoding features and membership degrees, effectively mitigating class boundary ambiguity and improving discriminability under limited-sample conditions. Extensive experiments on the MIT-BIH Arrhythmia Database demonstrate the superiority of the NCS framework. NCSN achieves 98.77% intra-patient accuracy and 87.82% inter-patient accuracy, while NCSNF further improves performance to 99.16% and 90.85%, respectively, outperforming existing baseline methods.
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He et al. (2026) studied Arrhythmia. NeuroCardioSense (NCS) framework vs. Existing baseline methods was evaluated on Intra-patient and inter-patient accuracy. The NeuroCardioSenseNet-Fusion (NCSNF) framework achieved 99.16% intra-patient accuracy and 90.85% inter-patient accuracy for ECG classification, outperforming existing baseline methods.
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