Information from postural/sleep position changes and body acceleration: A comparison of chest-worn sensors, wrist actigraphy, and polysomnography

Javad Razjouyan, Hyoki Lee, Sairam Parthasarathy, Martha J Mohler, Amir Sharafkhaneh, Bijan Najafi

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Study Objectives: To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes. Methods: Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist) and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland- Altman analysis. Results: Chest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy. Conclusions: Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.

Original languageEnglish (US)
Pages (from-to)1301-1310
Number of pages10
JournalJournal of Clinical Sleep Medicine
Volume13
Issue number11
DOIs
StatePublished - Jan 1 2017

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Actigraphy
Polysomnography
Wrist
Sleep
Thorax

Keywords

  • Chest sensor
  • Polysomnography
  • Sleep
  • Validation
  • Wearable sensor

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Neurology
  • Clinical Neurology

Cite this

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title = "Information from postural/sleep position changes and body acceleration: A comparison of chest-worn sensors, wrist actigraphy, and polysomnography",
abstract = "Study Objectives: To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes. Methods: Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8{\%} female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist) and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland- Altman analysis. Results: Chest identified sleep/wake epochs with an accuracy of on average 6{\%} higher than Wrist (85.8{\%} versus 79.8{\%}). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3){\%} for sleep efficacy. Conclusions: Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.",
keywords = "Chest sensor, Polysomnography, Sleep, Validation, Wearable sensor",
author = "Javad Razjouyan and Hyoki Lee and Sairam Parthasarathy and Mohler, {Martha J} and Amir Sharafkhaneh and Bijan Najafi",
year = "2017",
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T1 - Information from postural/sleep position changes and body acceleration

T2 - A comparison of chest-worn sensors, wrist actigraphy, and polysomnography

AU - Razjouyan, Javad

AU - Lee, Hyoki

AU - Parthasarathy, Sairam

AU - Mohler, Martha J

AU - Sharafkhaneh, Amir

AU - Najafi, Bijan

PY - 2017/1/1

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N2 - Study Objectives: To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes. Methods: Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist) and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland- Altman analysis. Results: Chest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy. Conclusions: Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.

AB - Study Objectives: To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes. Methods: Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist) and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland- Altman analysis. Results: Chest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy. Conclusions: Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.

KW - Chest sensor

KW - Polysomnography

KW - Sleep

KW - Validation

KW - Wearable sensor

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