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Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter

BACKGROUND: The calculation of arterial oxygen saturation (SpO(2)) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring. METHODS: A new method combining temporally constrained indep...

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Autores principales: Peng, Fulai, Zhang, Zhengbo, Gou, Xiaoming, Liu, Hongyun, Wang, Weidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021027/
https://www.ncbi.nlm.nih.gov/pubmed/24761769
http://dx.doi.org/10.1186/1475-925X-13-50
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author Peng, Fulai
Zhang, Zhengbo
Gou, Xiaoming
Liu, Hongyun
Wang, Weidong
author_facet Peng, Fulai
Zhang, Zhengbo
Gou, Xiaoming
Liu, Hongyun
Wang, Weidong
author_sort Peng, Fulai
collection PubMed
description BACKGROUND: The calculation of arterial oxygen saturation (SpO(2)) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring. METHODS: A new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters. RESULTS: Compared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods. CONCLUSIONS: This paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method.
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spelling pubmed-40210272014-05-28 Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter Peng, Fulai Zhang, Zhengbo Gou, Xiaoming Liu, Hongyun Wang, Weidong Biomed Eng Online Research BACKGROUND: The calculation of arterial oxygen saturation (SpO(2)) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring. METHODS: A new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters. RESULTS: Compared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods. CONCLUSIONS: This paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method. BioMed Central 2014-04-24 /pmc/articles/PMC4021027/ /pubmed/24761769 http://dx.doi.org/10.1186/1475-925X-13-50 Text en Copyright © 2014 Peng et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Peng, Fulai
Zhang, Zhengbo
Gou, Xiaoming
Liu, Hongyun
Wang, Weidong
Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
title Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
title_full Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
title_fullStr Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
title_full_unstemmed Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
title_short Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
title_sort motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021027/
https://www.ncbi.nlm.nih.gov/pubmed/24761769
http://dx.doi.org/10.1186/1475-925X-13-50
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