用Mamba网络提升可穿戴设备心率信号去噪效果
Robust Photoplethysmography Signal Denoising via Mamba Networks
- 基于Mamba架构设计时序建模的去噪主干网络
- 在真实运动干扰下仍保持心率估计准确率
- 适合可穿戴健康监测系统实际部署
光电容积脉搏波（PPG）广泛用于可穿戴健康监测，但噪声和运动伪影常导致信号失真，影响心率（HR）估计等下游应用。本文提出一种深度学习框架，以保留生理信息为关键目标。框架设计了基于Mamba的去噪主干网络DPNet，实现高效时序建模；引入尺度不变信噪比（SI-SDR）损失以提升波形保真度，并加入辅助心率预测器（HRP）通过心率监督增强生理一致性。在BIDMC数据集上的实验表明，该方法对合成噪声和真实运动伪影均具有强鲁棒性，优于传统滤波与现有神经模型。所提方法能有效恢复原始信号并维持心率精度，验证了SI-SDR损失与心率引导监督的互补作用。结果表明该方法具备在可穿戴医疗系统中实际部署的潜力。
原文摘要 · Abstract (English)
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.
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