From Motion Artifacts to Clinical Insight: Multi-Modal Deep Learning for Robust Arrhythmia Screening in Ambulatory ECG Monitoring.

Motion artifacts corrupt wearable ECG signals and generate false alarms of arrhythmias, limiting the clinical adoption of continuous cardiac monitoring. We present a dual-stream deep learning framework for motion-robust binary arrhythmia classification through multi-modal sensor fusion and multi-SNR training. ResNet-18 processes ECG spectrograms, while CNN-BiLSTM encodes accelerometer motion patterns; attention-gated fusion with gate diversity regularization adaptively weights modalities based on signal reliability. Training in MIT-BIH data augmented at three noise levels (24, 12, 6 dB) enables noise-invariant learning with successful generalization to unseen conditions. The framework achieves 99.5% accuracy under clean signals, gracefully degrading to 88.2% at extreme noise (-6 dB SNR)-a 46% improvement over training with single-SNR. The high gate diversity (σ>0.37) confirms adaptive context-dependent fusion. With a 0.09% false positive rate and real-time processing (238 beats/second), the system provides practical continuous arrhythmia screening, establishing the foundation for hierarchical monitoring systems where binary screening activates detailed multi-class diagnosis.