Towards pulse rate parametrization during free-living activities using smart wristband

Rapalis, Andrius; Petrėnas, Andrius; Šimaitytė, Monika; Bailón, Raquel; Marozas, Vaidotas

doi:10.1088/1361-6579/aac24a

Cited by 11 publications

(12 citation statements)

References 42 publications

(52 reference statements)

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“…We also observed the differences in PRV measured on the forearm and wrist even in the same arm [7]. Additionally, differing effects of body posture and exercise between PRV and HRV [8] and the variance of PRV caused by the effects of differing waveform on pulse wave fiducial point have been reported [9,10].…”

supporting

confidence: 62%

Pulse rate variability: a new biomarker, not a surrogate for heart rate variability

Yuda

Shibata

Ogata³

et al. 2020

J Physiol Anthropol

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With the popularization of pulse wave signals by the spread of wearable watch devices incorporating photoplethysmography (PPG) sensors, many studies are reporting the accuracy of pulse rate variability (PRV) as a surrogate of heart rate variability (HRV). However, the authors are concerned about their research paradigm based on the assumption that PRV is a biomarker that reflects the same biological properties as HRV. Because PPG pulse wave and ECG R wave both reflect the periodic beating of the heart, pulse rate and heart rate should be equal, but it does not guarantee that the respective variabilities are also the same. The process from ECG R wave to PPG pulse wave involves several transformation steps of physical properties, such as those of electromechanical coupling and conversions from force to volume, volume to pressure, pressure impulse to wave, pressure wave to volume, and volume to light intensity. In fact, there is concreate evidence that shows discrepancy between PRV and HRV, such as that demonstrating the presence of PRV in the absence of HRV, differences in PRV with measurement sites, and differing effects of body posture and exercise between them. Our observations in adult patients with an implanted cardiac pacemaker also indicate that fluctuations in R-R intervals, pulse transit time, and pulse intervals are modulated differently by autonomic functions, respiration, and other factors. The authors suggest that it is more appropriate to recognize PRV as a different biomarker than HRV. Although HRV is a major determinant of PRV, PRV is caused by many other sources of variability, which could contain useful biomedical information that is neither error nor noise.

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supporting

confidence: 62%

Pulse rate variability: a new biomarker, not a surrogate for heart rate variability

Yuda

Shibata

Ogata³

et al. 2020

J Physiol Anthropol

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show abstract

“…The ACAT algorithm we used to detect CVHR has been developed and optimized for ECG RRI [11][12][13], but the present study indicated that the ACAT algorithm can also be used for PPG PI and performs as well as for ECG RRI. Many studies have reported discrepancies and non-substitution of pulse rate and heart rate variability [15][16][17][18][19], particularly in various diseases including sleep apnea [26]. Nonetheless, the present study showed equivalence of PPG and ECG in the detection of sleep apnea.…”

Section: Discussionmentioning

confidence: 43%

“…In a previous study of 862 subjects undergoing polysomnographic examination, the hourly frequency of CVHR detected by the ACAT algorithm showed a correlation coefficient of 0.84 with the apneahypopnea index (AHI) and detected subjects with AHI ≥15 with 83% sensitivity and 88% specificity [11]. Although studies of pulse interval (PI) variability have reported important differences in the amplitude of short-term fluctuation components from those of R-R interval (RRI) variability [15][16][17][18][19], ACAT algorithm may be robust to such differences because the CVHR wave period is long (25-130 s) and ACAT has ability to adapt the detection threshold according to the changes in CVHR amplitude. 2020 and May 2020.…”

Section: Introductionmentioning

confidence: 99%

Quantitative detection of sleep apnea with wearable watch device

Hayano

Yamamoto

Nonaka

et al. 2020

Preprint

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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 by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4-28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

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“…The ACAT algorithm we used to detect CVHR has been developed and optimized for ECG RRI [11][12][13], but the present study indicated that the ACAT algorithm can also be used for PPG PI and performs as well as for ECG RRI. Many studies have reported discrepancies and non-substitution of pulse rate and heart rate variability [15][16][17][18][19], particularly in various diseases including sleep apnea [27]. Nonetheless, the present study showed equivalence of PPG and ECG in the detection of sleep apnea.…”

Section: Plos Onementioning

confidence: 39%

“…In a previous study of 862 subjects undergoing polysomnographic examination, the hourly frequency of CVHR detected by the ACAT algorithm showed a correlation coefficient of 0.84 with the apnea-hypopnea index (AHI) and detected subjects with AHI �15 with 83% sensitivity and 88% specificity [11]. Although studies of pulse interval (PI) variability have reported important differences in the amplitude of short-term fluctuation components from those of R-R interval (RRI) variability [15][16][17][18][19][20], ACAT algorithm may be robust to such differences because the cycle length of CVHR is long (25-130 s) and ACAT has ability to adapt the detection threshold according to the changes in CVHR amplitude.…”

Section: Introductionmentioning

confidence: 99%

Quantitative detection of sleep apnea with wearable watch device

et al. 2020

View full text Add to dashboard Cite

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 by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4–28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

show abstract

Towards pulse rate parametrization during free-living activities using smart wristband

Cited by 11 publications

References 42 publications

Pulse rate variability: a new biomarker, not a surrogate for heart rate variability

Pulse rate variability: a new biomarker, not a surrogate for heart rate variability

Quantitative detection of sleep apnea with wearable watch device

Quantitative detection of sleep apnea with wearable watch device

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