Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

被引：6
|
作者：
Guler, Saygun [1 ]
Golparvar, Ata [1 ,2 ]
Ozturk, Ozberk [1 ]
Dogan, Huseyin [3 ]
Yapici, Murat Kaya [1 ,4 ,5 ]
机构：
[1] Sabanci Univ, Fac Engn & Nat Sci, TR-34956 Istanbul, Turkiye
[2] Ecole Polytech Fed Lausanne EPFL, Integrated Circuit Lab, CH-2002 Neuchatel, Switzerland
[3] Bournemouth Univ, Dept Comp & Informat, Poole BH12 5BB, England
[4] Sabanci Univ, Nanotechnol & Applicat Ctr, TR-34956 Istanbul, Turkiye
[5] Univ Washington, Dept Elect Engn, Seattle, WA 98195 USA
来源：
BIOMEDICAL PHYSICS & ENGINEERING EXPRESS | 2023年 / 9卷 / 02期
关键词：
digital signal processing; heart rate; health monitoring; vital signs; photoplehysmography; image processing; human-computer interaction; NONCONTACT;
D O I：
10.1088/2057-1976/acaf8a
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still not straightforward. The best algorithm selection improves the signal-to-noise ratio (SNR) and therefore improves the accuracy of the recognition and classification of vital signs. We recorded more than 300 temporal rPPG signals where the noise was not motion-induced. Then, we investigated the best digital filter in pre-processing temporal rPPG data and compared the performances of 10 filters with 10 orders each (i.e., a total of 100 filters). The performances are assessed using a signal quality metric on three levels. The quality of the raw signals was classified under three categories; Q1 being the best and Q3 being the worst. The results are presented in SNR scores, which show that the Chebyshev II orders of 2nd, 4th, and 6th perform the best for denoising rPPG signals.
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页数：8
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