Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning
Authors: Guler, S., Golparvar, A., Ozturk, O., Dogan, H. and Kaya Yapici, M.
Journal: Biomedical Physics and Engineering Express
Volume: 9
Issue: 2
eISSN: 2057-1976
DOI: 10.1088/2057-1976/acaf8a
Abstract: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.
https://eprints.bournemouth.ac.uk/37954/
Source: Scopus
Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning.
Authors: Guler, S., Golparvar, A., Ozturk, O., Dogan, H. and Kaya Yapici, M.
Journal: Biomed Phys Eng Express
Volume: 9
Issue: 2
eISSN: 2057-1976
DOI: 10.1088/2057-1976/acaf8a
Abstract: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.
https://eprints.bournemouth.ac.uk/37954/
Source: PubMed
Optimal Digital Filter Selection for Remote Photoplethysmography (rPPG) Signal Conditioning
Authors: Guler, S., Golparvar, A., Ozturk, O., Dogan, H. and Yapici, M.K.
Journal: Biomedical Physics and Engineering Express
Publisher: Institute of Physics Publishing
ISSN: 2057-1976
DOI: 10.1088/2057-1976/acaf8a
https://eprints.bournemouth.ac.uk/37954/
Source: Manual
Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning.
Authors: Guler, S., Golparvar, A., Ozturk, O., Dogan, H. and Kaya Yapici, M.
Journal: Biomedical physics & engineering express
Volume: 9
Issue: 2
eISSN: 2057-1976
ISSN: 2057-1976
DOI: 10.1088/2057-1976/acaf8a
Abstract: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.
https://eprints.bournemouth.ac.uk/37954/
Source: Europe PubMed Central
Optimal Digital Filter Selection for Remote Photoplethysmography (rPPG) Signal Conditioning
Authors: Guler, S., Golparvar, A., Ozturk, O., Dogan, H. and Yapici, M.K.
Journal: Biomedical Physics & Engineering Express
Volume: 9
Publisher: Institute of Physics Publishing
ISSN: 2057-1976
Abstract: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 improves the accuracy of the recognition and classification of vital signs. We recorded more than 300 temporal rPPG recordings, where the noise is mainly not motion-induced. Then, we investigated the best digital filter in pre-processing temporal rPPG data and compare the performances of ten different filters with ten orders each (i.e., total 100 filters). The performances are assessed using a signal quality metric on three levels as the quality of the raw signals was classified under three categories; Q1 being the best 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.
https://eprints.bournemouth.ac.uk/37954/
Source: BURO EPrints