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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/28992827
Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography - PubMed Skip to main page content
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. 2017 Nov 15;13(11):1301-1310.
doi: 10.5664/jcsm.6802.

Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography

Javad Razjouyan  1 , Hyoki Lee  1 , Sairam Parthasarathy  2 , Jane Mohler  3 , Amir Sharafkhaneh  4 , Bijan Najafi  1
Affiliations

Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography

Javad Razjouyan et al. J Clin Sleep Med. .

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.

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Figures

Figure 1
Figure 1. Performance.
Pairwise comparison of individual sensitivity, specificity and accuracy in the chest sensor and the wrist sensor. Sensitivity represented epochs that the sensor correctly detected as sleep relative to gold standard. Specificity represented epochs that the sensor correctly detected as wake. Accuracy represented the rate of correct epoch detection. The wrist sensor represented by “Actiwatch” label, chest sensor algorithm optimized by Matthew correlation coefficient represented by “ChestMCC”, and chest sensor algorithm optimized by accuracy rate represented by “ChestACCU.”
Figure 2
Figure 2. Bland-Altman plots.
Bland-Altman plot of the sleep parameters (left column = total sleep time, middle column = wake after sleep onset, right column = sleep efficiency). The y-axis shows the difference of between gold standard (PSG) and sensor derived sleep parameters (PSG - Sensor). The x-axis shows the average of gold standard and sensor derived sleep parameters ([PSG + Sensor] / 2). The wrist sensor represented by “Wrist” label, chest sensor algorithm optimized by Matthew correlation coefficient represented by “ChestMCC”, and chest sensor algorithm optimized by accuracy rate represented by ChestACCU”. The blue lines (dash) show the average of the difference or bias. The red lines (dash and dot) show the average ± 1.96 × SD. The SD represents the precision. The lower values of bias and precision, the better agreement with the gold standard.
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