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Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals
Goal: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and min...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
Publicado: |
IEEE
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299207/ https://www.ncbi.nlm.nih.gov/pubmed/35873901 http://dx.doi.org/10.1109/OJEMB.2022.3174806 |
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collection | PubMed |
description | Goal: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject- and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1-scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35% on average for fiducial point identification and mean percent difference by 16% on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients’ daily life. |
format | Online Article Text |
id | pubmed-9299207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-92992072022-07-22 Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals IEEE Open J Eng Med Biol Article Goal: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject- and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1-scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35% on average for fiducial point identification and mean percent difference by 16% on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients’ daily life. IEEE 2022-05-12 /pmc/articles/PMC9299207/ /pubmed/35873901 http://dx.doi.org/10.1109/OJEMB.2022.3174806 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals |
title | Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals |
title_full | Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals |
title_fullStr | Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals |
title_full_unstemmed | Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals |
title_short | Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals |
title_sort | boosted-springdtw for comprehensive feature extraction of ppg signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299207/ https://www.ncbi.nlm.nih.gov/pubmed/35873901 http://dx.doi.org/10.1109/OJEMB.2022.3174806 |
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