Analysis of methods and algorithms for remote photoplethysmography signal diagnostic and filtering

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dc.citation.epage88
dc.citation.issue1
dc.citation.journalTitleДосягнення у кібер-фізичних системах
dc.citation.spage82
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorBerezhnyi, Ihor
dc.contributor.authorNakonechnyi, Adrian
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-03-17T10:08:00Z
dc.date.created2024-02-27
dc.date.issued2024-02-27
dc.description.abstractRemote photoplethysmography is becoming increasingly common in telemedicine for non-invasive physiological monitoring of the cardiovascular system. However, signal reliability has been reduced due to noise and artifacts, which requires reliable diagnostic and filtering methods. The research aim is to evaluate existing methods and algorithms for diagnosing and filtering remote photoplethysmography signals to improve the accuracy of human cardiovascular monitoring. A systematic review has identified methodologies for improving remote photoplethysmography signals by analyzing their principles, implementation, and effectiveness. Various approaches have been analyzed, including the use of statistical computing, adaptive filters, and machine learning algorithms. Each approach offers unique advantages and limitations in terms of noise reduction and artifact removal.
dc.format.extent82-88
dc.format.pages7
dc.identifier.citationBerezhnyi I. Analysis of methods and algorithms for remote photoplethysmography signal diagnostic and filtering / Berezhnyi Ihor, Nakonechnyi Adrian // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 1. — P. 82–88.
dc.identifier.citationenBerezhnyi I. Analysis of methods and algorithms for remote photoplethysmography signal diagnostic and filtering / Berezhnyi Ihor, Nakonechnyi Adrian // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 1. — P. 82–88.
dc.identifier.doidoi.org/10.23939/acps2024.01.082
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/64184
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofДосягнення у кібер-фізичних системах, 1 (9), 2024
dc.relation.ispartofAdvances in Cyber-Physical Systems, 1 (9), 2024
dc.relation.references[1] Nakonechnyi A., Berezhnyi I. (2023). Estimation of heart rate and its variability based on wavelet analysis of photoplethysmographic signals in real time. Intelligent data acquisition and advanced computing systems: technology and applications: proceedings of the 12th IEEE International conference IDAACS, Dortmund, Germany, 7–9 September 2023. Vol. 1, pp. 765–770. ISBN: 979-835035805-6. DOI: 10.1109/IDAACS58523.2023.10348785
dc.relation.references[2] Yonggang Tong, Zhipei Huang, Zhen Zhang, Ming Yin, Guangcun Shan, Jiankang Wu, Fei Qin (2023). Detailpreserving arterial pulse wave measurement based Biorthogonal wavelet decomposition from remote RGB observations. Measurement 2023, Vol. 222, pp. 123–128. ISSN 0263-2241. DOI: 10.1016/j.measurement.2023.113605.
dc.relation.references[3] Birla Lokendra, Gupta Puneet (2022). AND-rPPG: A novel denoising-rPPG network for improving remote heart rate estimation. Computers in Biology and Medicine, Vol. 141, 2022, pp. 169–181. ISSN 0010-4825. DOI: 10.1016/j.compbiomed.2021.105146.
dc.relation.references[4] Thayer J. F., Yamamoto S. S., Brosschot J. F. (2010). The relationship of autonomic imbalance, heart rate variability, and cardiovascular disease risk factors”. Int J Cardiol. 2010, pp. 122–141. DOI: 10.1016/j.ijcard.2009.09.543.
dc.relation.references[5] Wang W., den Brinker A.C., Stuijk S., de Haan G. (2016). Algorithmic principles of remote ppg. IEEE Trans. Biomed Eng. 2016, pp. 1479–1491. DOI: 10.1109/TBME.2016.2609282.
dc.relation.references[6] Farzana Anowar, Samira Sadaoui, Bassant Selim (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, Vol. 40, 2021, pp. 41–49. ISSN 1574-0137. DOI: doi.org/10.1016/j.cosrev.2021.100378.
dc.relation.references[7] Michael Schmid, David Rath, Ulrike Diebold (2020). Why and How Savitzky–Golay Filters Should Be Replaced. ACS Meas. Sci. Au., pp. 185–196. DOI: 10.13140/RG.2.2.20339.50725.
dc.relation.references[8] Lagun, Ilona (2019). The Methods of Choosing the Wavelets for One Dimensional Signal Processing. Advances in Cyber-physical Systems 2019; Vol. 4, No. 2, pp. 84–90. DOI: 10.23939/acps2019.02.084.
dc.relation.references[9] Hanguang Xiao, Tianqi Liu, Yisha Sun, Yulin Li, Shiyi Zhao, Alberto Avolio (2024). Remote photoplethysmography for heart rate measurement. Biomedical Signal Processing and Control, Vol.88, Part B, 2024, pp. 254–263. ISSN 1746-8094. DOI: 10.1016/j.bspc.2023.105608.
dc.relation.references[10] Mohammad Sabokrou, Masoud Pourreza, Xiaobai Li, Mahmood Fathy, Guoying Zhao (2021). Deep-HR: Fast heart rate estimation from face video under realistic conditions. Expert Systems with Applications, Vol. 186, pp. 73–83. ISSN 0957-4174. DOI: 10.1016/j.eswa.2021.1
dc.relation.referencesen[1] Nakonechnyi A., Berezhnyi I. (2023). Estimation of heart rate and its variability based on wavelet analysis of photoplethysmographic signals in real time. Intelligent data acquisition and advanced computing systems: technology and applications: proceedings of the 12th IEEE International conference IDAACS, Dortmund, Germany, 7–9 September 2023. Vol. 1, pp. 765–770. ISBN: 979-835035805-6. DOI: 10.1109/IDAACS58523.2023.10348785
dc.relation.referencesen[2] Yonggang Tong, Zhipei Huang, Zhen Zhang, Ming Yin, Guangcun Shan, Jiankang Wu, Fei Qin (2023). Detailpreserving arterial pulse wave measurement based Biorthogonal wavelet decomposition from remote RGB observations. Measurement 2023, Vol. 222, pp. 123–128. ISSN 0263-2241. DOI: 10.1016/j.measurement.2023.113605.
dc.relation.referencesen[3] Birla Lokendra, Gupta Puneet (2022). AND-rPPG: A novel denoising-rPPG network for improving remote heart rate estimation. Computers in Biology and Medicine, Vol. 141, 2022, pp. 169–181. ISSN 0010-4825. DOI: 10.1016/j.compbiomed.2021.105146.
dc.relation.referencesen[4] Thayer J. F., Yamamoto S. S., Brosschot J. F. (2010). The relationship of autonomic imbalance, heart rate variability, and cardiovascular disease risk factors". Int J Cardiol. 2010, pp. 122–141. DOI: 10.1016/j.ijcard.2009.09.543.
dc.relation.referencesen[5] Wang W., den Brinker A.C., Stuijk S., de Haan G. (2016). Algorithmic principles of remote ppg. IEEE Trans. Biomed Eng. 2016, pp. 1479–1491. DOI: 10.1109/TBME.2016.2609282.
dc.relation.referencesen[6] Farzana Anowar, Samira Sadaoui, Bassant Selim (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, Vol. 40, 2021, pp. 41–49. ISSN 1574-0137. DOI: doi.org/10.1016/j.cosrev.2021.100378.
dc.relation.referencesen[7] Michael Schmid, David Rath, Ulrike Diebold (2020). Why and How Savitzky–Golay Filters Should Be Replaced. ACS Meas. Sci. Au., pp. 185–196. DOI: 10.13140/RG.2.2.20339.50725.
dc.relation.referencesen[8] Lagun, Ilona (2019). The Methods of Choosing the Wavelets for One Dimensional Signal Processing. Advances in Cyber-physical Systems 2019; Vol. 4, No. 2, pp. 84–90. DOI: 10.23939/acps2019.02.084.
dc.relation.referencesen[9] Hanguang Xiao, Tianqi Liu, Yisha Sun, Yulin Li, Shiyi Zhao, Alberto Avolio (2024). Remote photoplethysmography for heart rate measurement. Biomedical Signal Processing and Control, Vol.88, Part B, 2024, pp. 254–263. ISSN 1746-8094. DOI: 10.1016/j.bspc.2023.105608.
dc.relation.referencesen[10] Mohammad Sabokrou, Masoud Pourreza, Xiaobai Li, Mahmood Fathy, Guoying Zhao (2021). Deep-HR: Fast heart rate estimation from face video under realistic conditions. Expert Systems with Applications, Vol. 186, pp. 73–83. ISSN 0957-4174. DOI: 10.1016/j.eswa.2021.1
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.rights.holder© Berezhnyi I., Nakonechnyi A., 2024
dc.subjectFiltering
dc.subjectPhotoplethysmography
dc.subjectHeart rate variability
dc.subjectWavelet transform
dc.titleAnalysis of methods and algorithms for remote photoplethysmography signal diagnostic and filtering
dc.typeArticle

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