Robust Photoplethysmography Signal Denoising via Mamba Networks
This addresses reliability issues in PPG-based health monitoring for wearable device users, representing an incremental improvement with a novel hybrid method.
The paper tackled the problem of noise and motion artifacts degrading photoplethysmography (PPG) signals in wearable health monitoring, proposing a deep learning framework called DPNet that achieved strong robustness and outperformed conventional filtering and existing neural models on the BIDMC dataset.
Photoplethysmography (PPG) is widely used in wearable health monitoring, but its reliability is often degraded by noise and motion artifacts, limiting downstream applications such as heart rate (HR) estimation. This paper presents a deep learning framework for PPG denoising with an emphasis on preserving physiological information. In this framework, we propose DPNet, a Mamba-based denoising backbone designed for effective temporal modeling. To further enhance denoising performance, the framework also incorporates a scale-invariant signal-to-distortion ratio (SI-SDR) loss to promote waveform fidelity and an auxiliary HR predictor (HRP) that provides physiological consistency through HR-based supervision. Experiments on the BIDMC dataset show that our method achieves strong robustness against both synthetic noise and real-world motion artifacts, outperforming conventional filtering and existing neural models. Our method can effectively restore PPG signals while maintaining HR accuracy, highlighting the complementary roles of SI-SDR loss and HR-guided supervision. These results demonstrate the potential of our approach for practical deployment in wearable healthcare systems.