# Robust Photoplethysmography Signal Denoising via Mamba Networks

arXiv: https://arxiv.org/abs/2510.11058 · HTML: https://mlsanity.com/paper/2510.11058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Jamba: A Hybrid Transformer-Mamba Language Model](https://mlsanity.com/paper/2403.19887/) (2024)
- [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024)
- [An Investigation of Incorporating Mamba for Speech Enhancement](https://mlsanity.com/paper/2405.06573/) (2024)
- [A Deep Learning & Fast Wavelet Transform-based Hybrid Approach for Denoising of PPG Signals](https://mlsanity.com/paper/2301.06549/) (2023)
- [MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal](https://mlsanity.com/paper/2409.18828/) (2024)
- [MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network](https://mlsanity.com/paper/2411.18902/) (2024)
- [MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution](https://mlsanity.com/paper/2412.04861/) (2024)

## Abstract (arXiv)

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.