Imaging photoplethysmography: towards effective physiological measurements

Since its conception decades ago, Photoplethysmography (PPG) the non-invasive opto-electronic technique that measures arterial pulsations in-vivo has proven its worth by achieving and maintaining its rank as a compulsory standard of patient monitoring. However successful, conventional contact monitoring mode is not suitable in certain clinical and biomedical situations, e.g., in the case of skin damage, or when unconstrained movement is required. With the advance of computer and photonics technologies, there has been a resurgence of interest in PPG and one potential route to overcome the abovementioned issues has been increasingly explored, i.e., imaging photoplethysmography (iPPG). The emerging field of iPPG offers some nascent opportunities in effective and comprehensive interpretation of the physiological phenomena, indicating a promising alternative to conventional PPG. Heart and respiration rate, perfusion mapping, and pulse rate variability have been accessed using iPPG. To effectively and remotely access physiological information through this emerging technique, a number of key issues are still to be addressed. The engineering issues of iPPG, particularly the influence of motion artefacts on signal quality, are addressed in this thesis, where an engineering model based on the revised Beer-Lambert law was developed and used to describe opto-physiological phenomena relevant to iPPG. An iPPG setup consisting of both hardware and software elements was developed to investigate its reliability and reproducibility in the context of effective remote physiological assessment. Specifically, a first study was conducted for the acquisition of vital physiological signs under various exercise conditions, i.e. resting, light and heavy cardiovascular exercise, in ten healthy subjects. The physiological parameters derived from the images captured by the iPPG system exhibited functional characteristics comparable to conventional contact PPG, i.e., maximum heart rate difference was <3 bpm and a significant (p < 0.05) correlation between both measurements were also revealed. Using a method for attenuation of motion artefacts, the heart rate and respiration rate information was successfully assessed from different anatomical locations even in high-intensity physical exercise situations. This study thereby leads to a new avenue for noncontact sensing of vital signs and remote physiological assessment, showing clear and promising applications in clinical triage and sports training. A second study was conducted to remotely assess pulse rate variability (PRV), which has been considered a valuable indicator of autonomic nervous system (ANS) status. The PRV information was obtained using the iPPG setup to appraise the ANS in ten normal subjects. The performance of the iPPG system in accessing PRV was evaluated via comparison with the readings from a contact PPG sensor. Strong correlation and good agreement between these two techniques verify the effectiveness of iPPG in the remote monitoring of PRV, thereby promoting iPPG as a potential alternative to the interpretation of physiological dynamics related to the ANS. The outcomes revealed in the thesis could present the trend of a robust non-contact technique for cardiovascular monitoring and evaluation

Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data

Object: It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a reliable pulse transit time (PTT) signal. Approach: This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit. All data sets include recordings of PPG and ECG using a commonly deployed patient monitor. We calculated the PTT signal offline. Main Results: We report a novel constant oscillatory pattern in the PTT signal and identify this pattern as a sawtooth artifact. We apply an approach based on the de-shape method to visualize, quantify and validate this sawtooth artifact. Significance: The PPG and ECG signals not designed for the PTT evaluation may contain unwanted artifacts. The PTT signal should be calibrated before analysis to avoid erroneous interpretation of its physiological meaning

Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring

Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring

Assessing mental stress from the photoplethysmogram: a numerical study.

OBJECTIVE: Mental stress is detrimental to cardiovascular health, being a risk factor for coronary heart disease and a trigger for cardiac events. However, it is not currently routinely assessed. The aim of this study was to identify features of the photoplethysmogram (PPG) pulse wave which are indicative of mental stress. APPROACH: A numerical model of pulse wave propagation was used to simulate blood pressure signals, from which simulated PPG pulse waves were estimated using a transfer function. Pulse waves were simulated at six levels of stress by changing the model input parameters both simultaneously and individually, in accordance with haemodynamic changes associated with stress. Thirty-two feature measurements were extracted from pulse waves at three measurement sites: the brachial, radial and temporal arteries. Features which changed significantly with stress were identified using the Mann-Kendall monotonic trend test. MAIN RESULTS: Seventeen features exhibited significant trends with stress in measurements from at least one site. Three features showed significant trends at all three sites: the time from pulse onset to peak, the time from the dicrotic notch to pulse end, and the pulse rate. More features showed significant trends at the radial artery (15) than the brachial (8) or temporal (7) arteries. Most features were influenced by multiple input parameters. SIGNIFICANCE: The features identified in this study could be used to monitor stress in healthcare and consumer devices. Measurements at the radial artery may provide superior performance than the brachial or temporal arteries. In vivo studies are required to confirm these observations

pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis

Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.orgComment: The manuscript was submitted to "Physiological Measurement" on September 5, 202