Can accelerometry data improve estimates of heart rate variability from wrist pulse PPG sensors?

Kos, Maciej; Li, Xuan; Khaghani-Far, Iman; Gordon, Christine M.; Pavel, Misha; Jimison, Holly

doi:10.1109/embc.2017.8037141

Cited by 11 publications

(6 citation statements)

References 27 publications

(25 reference statements)

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“…A spiroergometer (Cosmed Fitmate Med, Cosmed The Metabolic Company, Italy) and a bicycle ergometer (Ergo Bike Premium 8, Daum Electronic GmbH, Germany) were used to measure oxygen consumption during exercise. HR was measured using the Firstbeat system (Firstbeat Heartrate monitoring belts and Sports software v4.7.2.1; Firstbeat Technologies Ltd., Jyväskylä, Finland), a valid and widely used system designed to monitor HR (Bogdány, Boros, Szemerszky, & Köteles, 2016; Hallman, Mathiassen, & Lyskov, 2015; Kos et al., 2017; Parak & Korhonen, 2013; Parak, Uuskoski, Machek, & Korhonen, 2017). After a short warm‐up period (3 min, 25 Watt), the resistance of the ergometer was increased by 25 Watt/min until the subject reached his or her limit of tolerance.…”

Section: Methodsmentioning

confidence: 99%

Cardioceptive accuracy is associated with arousal but not with valence and perceived exertion under physical load

et al. 2020

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Section: Methodsmentioning

confidence: 99%

Cardioceptive accuracy is associated with arousal but not with valence and perceived exertion under physical load

et al. 2020

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“…During the first few minutes, the staff member checked the quality of the PPG signal in real time through the E4 application to ensure proper wrist placement as recommended by Empatica. Wrist-worn PPG sensors tend to be more accurate at rest than during exercise because of contamination from movement artifacts and often require accelerometry technology to measure consistent or repetitive movements to minimize influence of these artifacts [21,[44][45][46][47]. The PPG sensor in the Empatica E4 is designed to be robust against movement artifacts in that it can attenuate noise even when movements are not repetitive in nature, using an artifact removal technique based on a combination of multiple infrared light wavelengths [30].…”

Section: Heart Rate Variability Measurementmentioning

confidence: 99%

Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults’ Physical Function: Observational Study

Jeste¹,

Lee²,

Wu³

et al. 2019

JMIR Mhealth Uhealth

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Background Heart rate variability (HRV), or variation in beat-to-beat intervals of the heart, is a quantitative measure of autonomic regulation of the cardiovascular system. Low HRV derived from electrocardiogram (ECG) recordings is reported to be related to physical frailty in older adults. Recent advances in wearable technology offer opportunities to more easily integrate monitoring of HRV into regular clinical geriatric health assessments. However, signals obtained from ECG versus wearable photoplethysmography (PPG) devices are different, and a critical first step preceding their widespread use is to determine whether HRV metrics derived from PPG devices also relate to older adults’ physical function. Objective This study aimed to investigate associations between HRV measured with a wrist-worn PPG device, the Empatica E4 sensor, and validated clinical measures of both objective and self-reported physical function in a cohort of older adults living independently within a continuing care senior housing community. Our primary hypothesis was that lower HRV would be associated with lower physical function. In addition, we expected that HRV would explain a significant proportion of variance in measures of physical health status. Methods We evaluated 77 participants from an ongoing study of older adults aged between 65 and 95 years. The assessments encompassed a thorough examination of domains typically included in a geriatric health evaluation. We collected HRV data with the Empatica E4 device and examined bivariate correlations between HRV quantified with the triangular index (HRV TI) and 3 widely used and validated measures of physical functioning—the Short Physical Performance Battery (SPPB), Timed Up and Go (TUG), and Medical Outcomes Study Short Form 36 (SF-36) physical composite scores. We further investigated the additional predictive power of HRV TI on physical health status, as characterized by SF-36 physical composite scores and Cumulative Illness Rating Scale for Geriatrics (CIRS-G) scores, using generalized estimating equation regression analyses with backward elimination. Results We observed significant associations of HRV TI with SPPB (n=52; Spearman ρ=0.41; P=.003), TUG (n=51; ρ=−0.40; P=.004), SF-36 physical composite scores (n=49; ρ=0.37; P=.009), and CIRS-G scores (n=52, ρ=−0.43; P=.001). In addition, the HRV TI explained a significant proportion of variance in SF-36 physical composite scores (R2=0.28 vs 0.11 without HRV) and CIRS-G scores (R2=0.33 vs 0.17 without HRV). Conclusions The HRV TI measured with a relatively novel wrist-worn PPG device was related to both objective (SPPB and TUG) and self-reported (SF-36 physical composite) measures of physical function. In addition, the HRV TI explained additional variance in self-reported physical function and cumulative illness severity beyond traditionally measured aspects of physical health. Future steps include longitudinal tracking of changes in both HRV and physical function, which will add important insights regarding the predictive value of HRV as a biomarker of physical health in older adults.

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“…running) is added to the PPG signal, large portions of the time-series may be corrupted, and methods such as interpolation may greatly reduce the variability of the inter-beat intervals. The accelerometer signal can however be useful in assessing the quality of PPG signals when used for HRV estimation and avoid HRV analysis based on unreliable data [ 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

Estimation of heart rate variability from finger photoplethysmography during rest, mild exercise and mild mental stress

Singstad

Azulay

Bjurstedt

et al. 2021

Journal of Electrical Bioimpedance

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Due to the possibilities in miniaturization and wearability, photoplethysmography (PPG) has recently gained a large interest not only for heart rate measurement, but also for estimating heart rate variability, which is derived from ECG by convention. The agreement between PPG and ECG-based HRV has been assessed in several studies, but the feasibility of PPG-based HRV estimation is still largely unknown for many conditions. In this study, we assess the feasibility of HRV estimation based on finger PPG during rest, mild physical exercise and mild mental stress. In addition, we compare different variants of signal processing methods including selection of fiducial point and outlier correction. Based on five minutes synchronous recordings of PPG and ECG from 15 healthy participants during each of these three conditions, the PPG-based HRV estimation was assessed for the SDNN and RMSSD parameters, calculated based on two different fiducial points (foot point and maximum slope), with and without outlier correction. The results show that HRV estimation based on finger PPG is feasible during rest and mild mental stress, but can give large errors during mild physical exercise. A good estimation is very dependent on outlier correction and fiducial point selection, and SDNN seems to be a more robust parameter compared to RMSSD for PPG-based HRV estimation.

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Can accelerometry data improve estimates of heart rate variability from wrist pulse PPG sensors?

Cited by 11 publications

References 27 publications

Cardioceptive accuracy is associated with arousal but not with valence and perceived exertion under physical load

Cardioceptive accuracy is associated with arousal but not with valence and perceived exertion under physical load

Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults’ Physical Function: Observational Study

Estimation of heart rate variability from finger photoplethysmography during rest, mild exercise and mild mental stress

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