Assessment of changes in arterial compliance by photoplethysmography

Nitzan, Meir; Babchenko, Anatoly; Faib, Igor; Davidson, Elyad; Adler, David

doi:10.1109/eeei.2000.924425

Cited by 6 publications

(4 citation statements)

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“…A previous study reported that it is difficult to use the absolute value of PPG amplitude for comparison, because it can change according to the characteristics of body tissues and individual characteristics, such as race, skin color, fingernail color, and finger size ( Alian and Shelley, 2014 ). Moreover, PPG baseline is affected by respiration, vascular compliance, vascular tone, pain, and drug use ( Nitzan et al, 2000 ; Shelley et al, 2006 ; Shelley, 2007 ). The amplitude of the systolic peak, a representative characteristic of the PPG waveform, has been reported to have a significant correlation with microvascular expansion, and is in proportion to the cardiac output ( Dorlas and Nijboer, 1985 ; Murray and Foster, 1996 ).…”

Section: Resultsmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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Section: Resultsmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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“…There are already studies about the diagnosis of OSAS based on the detection of vasoconstriction using peripheral arterial tonometry [19][20][21], which is a similar physiologic signal. The relationship between autonomic nervous system and PPG has also been studied in [18,22].…”

Section: Introductionmentioning

confidence: 99%

Detection of decreases in the amplitude fluctuation of pulse photoplethysmography signal as indication of obstructive sleep apnea syndrome in children

Gil

Vergara

Laguna

2008

Biomedical Signal Processing and Control

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“…Multiple studies investigated the diagnosis of obstructive sleep apnea syndrome (OSAS) based on the detection of vasoconstriction, using peripheral arterial tonometry [13], [22], [23]. The relationship between autonomic nervous system and PPG was also studied in [12], [24]. Detection of sleep apnea and hypopnea, from PPG [25] and HRV were already explored in literature [26]- [29].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO$_2$ Signals

Lazazzera

Deviaene

Varon

et al. 2021

IEEE Trans. Biomed. Eng.

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In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (SpO2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP) and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments, was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.

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Assessment of changes in arterial compliance by photoplethysmography

Cited by 6 publications

References 1 publication

Photoplethysmogram Analysis and Applications: An Integrative Review

Photoplethysmogram Analysis and Applications: An Integrative Review

Detection of decreases in the amplitude fluctuation of pulse photoplethysmography signal as indication of obstructive sleep apnea syndrome in children

Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO$_2$ Signals

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