PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography.
Song R, Chen H, Cheng J, Li C, Liu Y, Chen X.
Song R, et al.
IEEE J Biomed Health Inform. 2021 May;25(5):1373-1384. doi: 10.1109/JBHI.2021.3051176. Epub 2021 May 11.
IEEE J Biomed Health Inform. 2021.
PMID: 33434140
In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance (CHROM) signals. Considering that the cardiac signal is quasi-periodic and has apparent time- …
In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse sign …