Right-left correlation of the sympathetically induced fluctuations of photoplethysmographic signal in diabetic and non-diabetic subjects

Buchs, Andreas; Slovik, Youval; Rapoport, Micha J.; Rosenfeld, C.; Khanokh, Boris; Nitzan, Meir

doi:10.1007/bf02345963

Cited by 41 publications

(34 citation statements)

References 20 publications

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“…There is similarity in the pulse characteristics between the right and left body sides but clear differences between the proximal and distal measurement sites. However, the degree of right to left side similarity in the high and low frequency components of the PPG waveform can be reduced in patients with vascular disease (Allen and Murray 2000a) and also in patients with autonomic dysfunction (Buchs et al 2005).…”

Section: Measurement Protocol and Reproducibilitymentioning

confidence: 99%

Photoplethysmography and its application in clinical physiological measurement

2007

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Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

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Section: Measurement Protocol and Reproducibilitymentioning

confidence: 99%

Photoplethysmography and its application in clinical physiological measurement

2007

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“…Multi-sensor PPG systems have been developed to monitor blood volume changes at different sites simultaneously [34]–[36]. With the emphasis on detecting the right-to-left side physiological differences in clinical applications, these multi-sensor systems typically require electronic and optical matching of each PPG sensor as well as high temporal synchronization and anatomically symmetric wearing locations (e.g., both earlobes, both index fingers, or both legs) [35].…”

Section: Photoplethysmographymentioning

confidence: 99%

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Sun

Thakor

2016

IEEE Trans. Biomed. Eng.

461

238

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Photoplethysmography (PPG) is a non-invasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

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“…Participants were instructed on how to wear the band and at what tightness (based on arm circumference). It was decided to standardise the location of the PPG sensor in this study to the right wrist as differences in PPG are minimal across wrists [36,37]. Participants were asked to wear the devices for 24-hours and carry on with their regular working patterns and routine.…”

Section: Datamentioning

confidence: 99%

Estimating blood pressure trends and the nocturnal dip from photoplethysmography

et al. 2019

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Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24-hour blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines.Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure (BP) were obtained with a 24-hour ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long-and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip.Main results: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12±2.20 ∆mmHg and a correlation of 0.69 (p = 3 * 10 −5 ). This dip was derived from trend estimates of BP which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip.Significance: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24hour measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

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Right-left correlation of the sympathetically induced fluctuations of photoplethysmographic signal in diabetic and non-diabetic subjects

Cited by 41 publications

References 20 publications

Photoplethysmography and its application in clinical physiological measurement

Photoplethysmography and its application in clinical physiological measurement

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Estimating blood pressure trends and the nocturnal dip from photoplethysmography

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