Abstract:The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed… Show more
“…It can also be applied to longer ECG monitoring (7 days) to reveal night-to-night variability in SA severity that is difficult to detect with other methods [ 75 ]. Furthermore, this method can be applied to pulse interval data obtained with wearable watch-shape pulse wave sensors [ 76 ], as described below. Fig.…”
Section: Introductionmentioning
confidence: 99%
“…PRV may contain useful biomedical information, but finding it requires an approach beyond the classical framework. For example, nocturnal PRV can be used to screen for SA by detecting cyclic variation of pulse rate (CVPR) [ 76 ]. In 41 patients who underwent diagnostic polysomnography (PSG) for SA, PPG was recorded simultaneously with a wearable watch device.…”
In the assessment of autonomic function by heart rate variability (HRV), the framework that the power of high-frequency component or its surrogate indices reflects parasympathetic activity, while the power of low-frequency component or LF/HF reflects sympathetic activity has been used as the theoretical basis for the interpretation of HRV. Although this classical framework has contributed greatly to the widespread use of HRV for the assessment of autonomic function, it was obtained from studies of short-term HRV (typically 5‑10 min) under tightly controlled conditions. If it is applied to long-term HRV (typically 24 h) under free-running conditions in daily life, erroneous conclusions could be drawn. Also, long-term HRV could contain untapped useful information that is not revealed in the classical framework. In this review, we discuss the limitations of the classical framework and present studies that extracted autonomic function indicators and other useful biomedical information from long-term HRV using novel approaches beyond the classical framework. Those methods include non-Gaussianity index, HRV sleep index, heart rate turbulence, and the frequency and amplitude of cyclic variation of heart rate.
“…It can also be applied to longer ECG monitoring (7 days) to reveal night-to-night variability in SA severity that is difficult to detect with other methods [ 75 ]. Furthermore, this method can be applied to pulse interval data obtained with wearable watch-shape pulse wave sensors [ 76 ], as described below. Fig.…”
Section: Introductionmentioning
confidence: 99%
“…PRV may contain useful biomedical information, but finding it requires an approach beyond the classical framework. For example, nocturnal PRV can be used to screen for SA by detecting cyclic variation of pulse rate (CVPR) [ 76 ]. In 41 patients who underwent diagnostic polysomnography (PSG) for SA, PPG was recorded simultaneously with a wearable watch device.…”
In the assessment of autonomic function by heart rate variability (HRV), the framework that the power of high-frequency component or its surrogate indices reflects parasympathetic activity, while the power of low-frequency component or LF/HF reflects sympathetic activity has been used as the theoretical basis for the interpretation of HRV. Although this classical framework has contributed greatly to the widespread use of HRV for the assessment of autonomic function, it was obtained from studies of short-term HRV (typically 5‑10 min) under tightly controlled conditions. If it is applied to long-term HRV (typically 24 h) under free-running conditions in daily life, erroneous conclusions could be drawn. Also, long-term HRV could contain untapped useful information that is not revealed in the classical framework. In this review, we discuss the limitations of the classical framework and present studies that extracted autonomic function indicators and other useful biomedical information from long-term HRV using novel approaches beyond the classical framework. Those methods include non-Gaussianity index, HRV sleep index, heart rate turbulence, and the frequency and amplitude of cyclic variation of heart rate.
“…To capture features of the electrical activity of the brain and the health of muscles and the nerve cells, electroencephalogram (EEG) and electromyography (EMG) sensors are used [ 13 – 19 ]. Blood volume pulse (BVP) can be captured using an optical photoplethysmography (PPG) sensor to estimate heart rate and heart rate variation as in [ 1 , 20 – 22 ]. PPG sensor [ 23 ] is also used to give an approximation for the oxygen saturation in blood ( SpO 2 ) as in [ 22 , 24 ].…”
Section: Wearable Devices and Machine Learningmentioning
confidence: 99%
“…Wearable devices may include different types of sensors to continuously monitor various human signals, e.g., temperature sensors, accelerometers, optical sensors, and biometric sensors. Although the readings of some of these sensors are not yet as accurate as stationary devices in hospitals, they are sometimes considered acceptable [ 1 , 2 ], depending on the application.…”
Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.
“…Hayano et al [ 50 ] also explored a pulse rate-based algorithm, extracted from a PPG wearable watch device to detect OSA, in 41 patients undergoing diagnostic PSG for OSA. An automated algorithm adapted from an algorithm developed for ECG analysis to detect a characteristic HR pattern related to sleep apnea episodes, called auto-correlated wave detection with adaptive threshold (ACAT), was applied on PPG pulse interval data obtained from 41 patients undergoing diagnostic PSG.…”
Section: Photoplethysmography Applications In Sleep-disordered Breathing Diagnosismentioning
This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.
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