| Citation: | LI Wen-tong, ZHANG Qi-qi, LIU Long-xin, MA Zhen-long, SUN Yun-jie, JI Xiao-qiang. Denoising of imaging photoplethysmography signals[J]. Chinese Optics. doi: 10.37188/CO.2025-0103
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Image Photoplethysmography (IPPG) signals are easily disturbed by noise during acquisition. To address the issue, this study proposes a denoising diffusion probability model for IPPG (DDPM-IPPG). This model eliminates baseline drift and noise through diffusion and reverse diffusion stages, and improves the signal-to-noise ratio and heart rate accuracy. First, Gaussian noise is gradually added to the photoplethysmography (PPG) signal during the diffusion phase to create a noise sequence. A noise predictor based on a nonlinear fusion module and a bridging module is trained. Subsequently, in the reverse diffusion phase, the well-trained noise predictor is employed to perform step-by-step denoising on the initially extracted IPPG signal. Through this denoising, a signal with high signal-to-noise ratio is recovered. The model proposed in this paper is validated and compared with current mainstream algorithms on the PURE, UBFC-IPPG, UBFC-Phys, and MMPD datasets. The experimental results show that DDPM-IPPG improves the signal-to-noise ratio by 1.06 dB on the PURE dataset comparing with the existing highest-precision extraction method. The mean absolute error of heart rate decreases by 0.24 bpm. The root mean square error of heart rate decreases by 0.41 bpm. On the UBFC-IPPG dataset, the signal-to-noise ratio is improved by 1.50 dB. The proposed DDPM-IPPG model has achieved the current advanced level in eliminating baseline drift and noise from IPPG signals, enabling a more precise approximation of the true signals and providing a more reliable data foundation for physiological health assessment and telemedicine monitoring.
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