Redundant Gaussian dictionary in compressed sensing for ambulatory photoplethysmography monitoring

作者: Kan Luo , Ying Ma , Jianxing Li , Fumin Zou , Xiao Liu

DOI: 10.1016/J.BSPC.2021.102479

关键词:

摘要: Abstract With the rapid development of wireless communication technology, photoplethysmography sensors have emerged and been applied in clinics or daily healthcare. However, energy efficiency is still a major obstacle for such devices long-term use. Data compression can reduce airtime over energy-hungry links improve sensors. In this study, we explored redundant dictionary-based compressed sensing scheme ambulatory data compression. We first proposed Gaussian dictionary, which was column combination circle shift atoms function. Besides, came up with simple method dictionary generation. To demonstrate its improvement compactly representing signal, compared dictionaries discrete cosine transform, wavelet Fourier Gabor transform experiments using database IEEE Signal Processing Cup 2015. Results indicated recovered signals were essentially undistorted by sparse binary measurement matrix, smoothed l0 pseudo norm recovery algorithm. The average percentage root-mean-square difference 8.00%, ratio 9% 0.32 when equal to 30%. Furthermore, after 20 Hz anti-aliasing low-pass filtering, perfect full-band our method, where every frame satisfied requirement non-distortion diagnosis at 70% reduction. outstanding performance low-complexity indicate an excellent choice enabled photoplethysmography.

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