Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning.
| Autor: | Guler S; Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey., Golparvar A; Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey.; Integrated Circuit Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), 2002 Neuchâtel, Switzerland., Ozturk O; Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey., Dogan H; Department of Computing and Informatics, Bournemouth University, BH12 5BB, United Kingdom., Kaya Yapici M; Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey.; Sabanci University Nanotechnology and Application Center, Sabanci University, 34956 Istanbul, Turkey.; Department of Electrical Engineering, University of Washington, 98195 Washington, United States of America. |
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| Jazyk: | angličtina |
| Zdroj: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2023 Jan 11; Vol. 9 (2). Date of Electronic Publication: 2023 Jan 11. |
| DOI: | 10.1088/2057-1976/acaf8a |
| Abstrakt: | 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. (© 2023 IOP Publishing Ltd.) |
| Databáze: | MEDLINE |
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