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

Motion artifacts reduction in cardiac pulse signal acquired from video imaging

This study examines the possibility of remotely measuring the cardiac pulse activity of a patient, which could be an alternative technique to the classical method. This type of measurement is non-invasive. However, several limitations may deteriorate the accuracy of the results, including changes in ambient illumination, motion artifacts (MA) and other interferences that may occur through video recording. The paper in hand presents a new approach as a remedy for the aforementioned problem in cardiac pulse signals extracted from facial video recordings. Partitioning provides the basis for the presented MA reduction method; the acquired signals are partitioned into two sets for each second and every partition is shifted to the mean level and then all the partitions are recombined again into one signal, which is followed by low-pass filtering for enhancement. The proposed compared with ordinary pulse oximetry Photoplethysmographic (PPG) method. The resulted correlation coefficient was found (0.957) when calculated between the results of the proposed method and the ordinary one. Experiments were implemented using a common camera by creating a dataset from 11 subjects. The ease of implementation of this method with a simple that can be used to monitor the cardiac pulse rates in both home and the clinical environments

Photoplethysmography based remote health monitoring system

One of the world's most leading killer diseases is the cardiovascular disease, which accounts for 16.7 million deaths annually. Out of the total population in the world, about 22 million people run the risk of sudden heart failure. However, saving the lives of cardiac patients can be improved by the emergency monitoring so that the initiation of treatment can be taken up within the crucial hour. The acquired signals by pulse oximetry provide significant information about the heart-rate, arterial blood oxygenation, blood pressure and respiratory-rate. Telemedicine provides a great impact in the emergency monitoring of patients located in remote nonclinical environments. A home cardiac telemedicine emergency system based on photoplethysmography has been developed. The acquired signals are processed, transmitted and stored in a local PC. Finally, the data are sent to the remote terminal located at the hospital through internet. The diagnoses are done by specialists from the reading and action can be immediately taken in emergency cases

A Review of Wearable Multi-wavelength Photoplethysmography

Optical pulse detection photoplethysmography (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics

A Review of Wearable Multi-wavelength Photoplethysmography

Optical pulse detection photoplethysmography (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics

Optimization of multi-wavelength Photoplethysmographic for wearable heart rate acquisition

Photoplethysmographic is an optical measure technique for heart rate monitoring on the surface of the skin. PPG based wearable heart rate monitor has become popular in consumer targeted market. This thesis work is based on the PulseOn product development and the final implementation will be integrated into the PulseOn OHRM sensor product. Choice of the wavelength of PPG is a trade-off between power consumption and accuracy considering the activity type, skin color and skin perfusion. The subject of this thesis is implementing a channel selection algorithm, which is green and IR channel, on a commercially available PulseOn wrist band to optimize the power consumption and accuracy of the measurement. The channel selection algorithm is first implemented and evaluated in Matlab simulation and then implemented in C code. Performance of the channel selection algorithm on the device is evaluated considering various factors, including skin color, tightness of the wristband. The results show that channel selection algorithm can not only reduce the power consumption but also help to handle the measurement on different measurement conditions

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data

The increasing popularity of smartwatches as affordable and longitudinal monitoring devices enables us to capture photoplethysmography (PPG) sensor data for detecting Atrial Fibrillation (AF) in real-time. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provide the uncertainty estimate of the prediction. Bayesbeat is efficient, robust, flexible, and highly scalable which makes it particularly suitable for deployment in commercially available wearable devices. Extensive experiments on a recently published large dataset reveal that our proposed method BayesBeat substantially outperforms the existing state-of-the-art methods.Comment: 8 pages, 5 figure