Survey on regression analysis of photoplethysmography using machine learning

Subashini, V.; Janaki, R.; Valarmathi, G.; Suganthi, S Regina Lourdhu; Prabha, R.; Sivasankari, K.; Kavitha, S.

doi:10.1016/j.matpr.2021.02.013

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…The extracted features can be used to determine various physiological parameters such as heart rate ( Fu et al, 2008 ), heart rate variability ( Gil et al, 2010 ), systolic blood pressure ( Suzuki and Ryu, 2014 ), and other cardiovascular parameters ( Charlton et al, 2022b ). The features can further be used for machine learning, and deep learning to develop predictive models for various cardiovascular diseases ( Liang et al, 2018b ; Khalid et al, 2018 ; Subashini et al, 2020 ; Allen et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

Abdullah

Hafid

Folke

et al. 2023

Front. Bioeng. Biotechnol.

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Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

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Section: Methodsmentioning

confidence: 99%

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

Abdullah

Hafid

Folke

et al. 2023

Front. Bioeng. Biotechnol.

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show abstract

“…A large number of studies have verified that a large amount of cardiovascular information is contained in the PPG signal, which is strongly correlated to blood pressure (Mukherjee et al, 2018). The morphological analysis of PPG has been applied to vascular assessment (Fedotov, 2019a), providing rich information for cardiovascular analysis (Fedotov, 2019b;Subashini et al, 2021). There were also some studies that use morphological characteristics of not only PPG but also ECG (Electrocardiogram) signals to jointly estimate blood pressure, and to estimate SBP value every 30 s Sun et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Advances in Basic and Applied Research in Photoplethysmography

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…A large number of studies have verified that a large amount of cardiovascular information is contained in the PPG signal, which is strongly correlated to blood pressure ( Mukherjee et al, 2018 ). The morphological analysis of PPG has been applied to vascular assessment ( Fedotov, 2019a ), providing rich information for cardiovascular analysis ( Fedotov, 2019b ; Subashini et al, 2021 ). There were also some studies that use morphological characteristics of not only PPG but also ECG (Electrocardiogram) signals to jointly estimate blood pressure, and to estimate SBP value every 30 s ( Sun et al, 2016 ; Sun et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

The identification of blood pressure variation with hypovolemia based on the volume compensation method

Chen

Xiao

et al. 2023

Front. Physiol.

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Objective: The purpose of this study is to identify the blood pressure variation, which is important in continuous blood pressure monitoring, especially in the case of low blood volume, which is critical for survival.Methods: A pilot study was conducted to identify blood pressure variation with hypovolemia using five Landrace pigs. New multi-dimensional morphological features of Photoplethysmography (PPG) were proposed based on experimental study of hemorrhagic shock in pigs, which were strongly correlated with blood pressure changes. Five machine learning methods were compared to develop the blood pressure variation identification model.Results: Compared with the traditional blood pressure variation identification model with single characteristic based on single period area of PPG, the identification accuracy of mean blood pressure variation based on the proposed multi-feature random forest model in this paper was up to 90%, which was 17% higher than that of the traditional blood pressure variation identification model.Conclusion: By the proposed multi-dimensional features and the identification method, it is more accurate to detect the rapid variation in blood pressure and to adopt corresponding measures.Significance: Rapid and accurate identification of blood pressure variation under low blood volume ultimately has the potential to effectively avoid complications caused by abnormal blood pressure in patients with clinical bleeding trauma.

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Survey on regression analysis of photoplethysmography using machine learning

Cited by 4 publications

References 5 publications

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

Advances in Basic and Applied Research in Photoplethysmography

The identification of blood pressure variation with hypovolemia based on the volume compensation method

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