用PPG信号直接估计呼吸波形，精度更高且无需人工特征
RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signals
- 多尺度RNN扩散模型端到端处理PPG信号
- 在BIDMC数据集上呼吸率误差低至1.18bpm
- 适合可穿戴设备实时健康监测
呼吸频率（RR）是重要的健康指标，但传统监测方式不便，难以持续进行。光电容积脉搏波描记法（PPG）传感器广泛集成于可穿戴设备中，为便携式连续呼吸率估计提供了可能。本文提出RespDiff，一种端到端的多尺度RNN扩散模型，用于从PPG信号中估计呼吸波形。该模型无需手工设计特征或剔除低质量信号段，适用于真实场景。通过多尺度编码器提取不同分辨率特征，并采用双向RNN处理PPG信号以提取呼吸波形。此外，引入频谱损失项进一步优化模型。在BIDMC数据集上的实验表明，RespDiff优于多项先前方法，呼吸率估计平均绝对误差（MAE）达1.18 bpm，显著低于其他方法的1.66至2.15 bpm，展现出在真实应用中实现鲁棒、高精度呼吸监测的潜力。
原文摘要 · Abstract (English)
Respiratory rate (RR) is a critical health indicator often monitored under inconvenient scenarios, limiting its practicality for continuous monitoring. Photoplethysmography (PPG) sensors, increasingly integrated into wearable devices, offer a chance to continuously estimate RR in a portable manner. In this paper, we propose RespDiff, an end-to-end multi-scale RNN diffusion model for respiratory waveform estimation from PPG signals. RespDiff does not require hand-crafted features or the exclusion of low-quality signal segments, making it suitable for real-world scenarios. The model employs multi-scale encoders, to extract features at different resolutions, and a bidirectional RNN to process PPG signals and extract respiratory waveform. Additionally, a spectral loss term is introduced to optimize the model further. Experiments conducted on the BIDMC dataset demonstrate that RespDiff outperforms notable previous works, achieving a mean absolute error (MAE) of 1.18 bpm for RR estimation while others range from 1.66 to 2.15 bpm, showing its potential for robust and accurate respiratory monitoring in real-world applications.
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