DCPA-SNN, Direct-Coding-Physics-Aware Spiking Neural Network: A Framework for Wearable ECG Denoising Under Dynamic-Noise Conditions

Smart wearable electrocardiogram (ECG) monitoring enables continuous cardiac assessment beyond clinical settings, but ECG signals are often degraded by baseline wander, muscle artifacts, and electrode motion artifacts. At present, pure end-to-end spiking neural networks (SNNs), with the advantage of low computational complexity, have rarely been explored for ECG noise suppression, particularly under dynamic conditions. To address this gap, this study proposes Direct-Coding-Physics-Aware Spiking Neural Network (DCPA-SNN) for wearable ECG denoising. The proposed method integrates a direct-coding SNN, channel attention, residual noise learning, and a physics-aware multi-domain loss function to preserve diagnostically important waveform structures. Clean ECG signals from the MIT-BIH Arrhythmia Database and real-noise segments from the MIT-BIH Noise Stress Test Database were used to construct single-noise and mixed-noise evaluation scenarios with input SNRs ranging from −6 dB to 4 dB to reflect the noise characteristics of wearable devices. Experimental results demonstrate that DCPA-SNN achieves robust denoising performance under different noise conditions. In the mixed-noise scenario, which serves as the primary evaluation setting of this study, the average denoised SNR reached 5.80 dB, with an average SNR improvement of 6.80 dB, while the R-peak detection rate increased from 90.71% to 95.72%. These results demonstrate that the proposed model, DCPA-SNN, provides a promising approach for wearable ECG denoising with potential for low-power deployment.