Photoplethysmography—new applications for an old technology: a sleep technology review

Ryals, Scott; Chiang, An Jen; Schutte-Rodin, Sharon; Chandrakantan, Arvind; Verma, Nitun; Holfinger, Steven; Abbasi-Feinberg, Fariha; Bandyopadhyay, Anuja; Baron, Kelly Glazer; Bhargava, Sumit; He, Ken; Kern, Joseph; Miller, Jennifer; Patel, Ruchir; Ratnasoma, Dulip; Deak, Maryann C.

doi:10.5664/jcsm.10300

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…[44,45] Additionally, the PPG signal consists of a direct current steady part, reflecting baseline blood volume, and an alternating current (AC) pulsatile part, representing variations in blood volume with each heartbeat. [46] By analyzing these compo-nents, valuable information can be extracted, including heart rate from pulse rate in the AC component or blood pressure trends by evaluating the amplitude and morphology of the systolic and diastolic phases. [47]…”

Section: Photoplethysmogram (Ppg)mentioning

confidence: 99%

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

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Section: Photoplethysmogram (Ppg)mentioning

confidence: 99%

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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“…Reflectance-based photoplethysmography (PPG) detects pulsatile changes in blood volume in peripheral tissues and has been defined as an important technology in sleep monitoring devices (Ryals et al, 2023). Peripheral artery tonometry refers to the determination of peripheral arterial vascular tone (the net balance between vasoconstriction and vasodilation) using PPG data.…”

Section: Photoplethysmography and Peripheral Arterial Tonometry For D...mentioning

confidence: 99%

“…This information is not currently available from peripheral artery tonometry-based devices, but could be detected using fingertip PPG data. The fingertip PPG signal inherently contains respiratory information because blood flow to body extremities is influenced by alterations in thoracic pressure throughout the respiratory cycle (Ryals et al, 2023). Therefore, the PPG signal amplitude oscillates in synchrony with the respiratory cycle.…”

Section: Detection Of Central Sleep Apnea and Rem Sleep Using Periphe...mentioning

confidence: 99%

A new approach to streamline obstructive sleep apnea therapy access using peripheral arterial tone-based home sleep test devices

Zou,

Vits,

Egea

et al. 2023

Front. Sleep

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Obstructive sleep apnea (OSA) is a prevalent condition that negatively impacts cardiovascular, metabolic and mental health. A high proportion of individuals with OSA remain undiagnosed and incur significant healthcare costs. The gold standard OSA diagnostic is in-lab polysomnography, but this is costly and time-consuming. Home sleep apnea tests (HSATs), including cardiorespiratory polygraphy and peripheral artery tonometry technology, provide an alternative. Advances in HSAT technology include non-invasive, easy-to-use medical devices that could allow unobtrusive, accessible, multi-night, cost-effective diagnosis and management of sleep-disordered breathing. One type of these devices is based on determination of peripheral arterial tone, and use photoplethysmography signals from the finger (oxygen saturation, pulse wave amplitude and pulse rate). The devices contain algorithms that use these data to generate the traditional metrics required by the American Academy of Sleep Medicine. They can be used to record sleep parameters over multiple nights at home, and can also provide information on total sleep time (TST) and sleep stages (including time spent in rapid eye movement sleep). The combination of objective measures (apnea-hypopnea index, oxygen desaturation index, respiratory disturbance index, TST) and subjective measures (symptoms and other patient-reported outcome measures) could facilitate the development of a personalized therapeutic plan for OSA patients. It is anticipated that the streamlined digital pathway facilitated by new peripheral artery tone-based technology could contribute to reducing the underdiagnosis of OSA, accelerating access to appropriate treatment, and the optimization of OSA therapy.

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“…Photoplethysmography (PPG) is an established, noninvasive technology that interrogates tissue using specific wavelengths of light, with photodetectors capturing light either reflected from or transmitted through a tissue bed. The measured signal is amplified, filtered, and processed to produce the characteristic PPG waveform synchronized to each heartbeat [ 1 ]. The shape of the PPG pulse wave is influenced by chronological age [ 2–4 ], health conditions such as hypertension [ 5 ], diabetes [ 6 ], obstructive sleep apnea [ 7 ], insomnia [ 8 ], and autonomic nervous system (ANS) activity [ 9–11 ].…”

Section: Introductionmentioning

confidence: 99%

Insights into vascular physiology from sleep photoplethysmography

Yılmaz

Ong

Ling

et al. 2023

Sleep

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Study Objectives Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG-pulse waveform during sleep alongside measurements of HRV and blood pressure. Methods Seventy eight healthy adults (50% male, median (range) age: 29.5 (23.0, 43.8)) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring (ABPM) and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated. Results Blood pressure, heart rate and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered heart rate, ∆T_norm, Rslope and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office blood pressure and sleep efficiency. Conclusion The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

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Photoplethysmography—new applications for an old technology: a sleep technology review

Cited by 13 publications

References 30 publications

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

A new approach to streamline obstructive sleep apnea therapy access using peripheral arterial tone-based home sleep test devices

Insights into vascular physiology from sleep photoplethysmography

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