成像式光体积描记术信号去噪OA

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 photoplethysmo-graphy(PPG)signal during the diffusion phase to create a noise sequence.A noise predictor based on a non-linear 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 pa-per 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 IP-PG signals,enabling a more precise approximation of the true signals and providing a more reliable data foundation for physiological health assessment and telemedicine monitoring.