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Khademi A, El-Manzalawy Y, Master L, Buxton OM, Honavar VG. Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach. Nat Sci Sleep 2019; 11:387-399. [PMID: 31849551 PMCID: PMC6912004 DOI: 10.2147/nss.s220716] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 10/21/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep. PURPOSE To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters. PARTICIPANTS AND METHODS We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses. RESULTS Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant. CONCLUSION Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.
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Affiliation(s)
- Aria Khademi
- College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA.,Artificial Intelligence Research Laboratory, The Pennsylvania State University, University Park, PA, USA.,Center for Big Data Analytics and Discovery Informatics, The Pennsylvania State University, University Park, PA, USA
| | - Yasser El-Manzalawy
- College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA.,Department of Imaging Science and Innovation, Geisinger Health System, Danville, PA, 17822, USA
| | - Lindsay Master
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Orfeu M Buxton
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA.,Clinical and Translational Sciences Institute, The Pennsylvania State University, University Park, PA, USA.,Division of Sleep Medicine, Harvard University, Boston, MA, USA.,Department of Social and Behavioral Sciences, Harvard Chan School of Public Health, Boston, MA, USA.,Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Vasant G Honavar
- College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA.,Artificial Intelligence Research Laboratory, The Pennsylvania State University, University Park, PA, USA.,Center for Big Data Analytics and Discovery Informatics, The Pennsylvania State University, University Park, PA, USA.,Clinical and Translational Sciences Institute, The Pennsylvania State University, University Park, PA, USA.,Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, USA.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
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Razjouyan J, Lee H, Parthasarathy S, Mohler J, Sharafkhaneh A, Najafi B. 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. J Clin Sleep Med 2017; 13:1301-1310. [PMID: 28992827 DOI: 10.5664/jcsm.6802] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 08/10/2017] [Indexed: 01/09/2023]
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.
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Affiliation(s)
- Javad Razjouyan
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Hyoki Lee
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Sairam Parthasarathy
- UAHS Center for Sleep and Circadian Science, University of Arizona, Tucson, Arizona
| | - Jane Mohler
- Arizona Center on Aging, College of Medicine, University of Arizona, Tucson, Arizona
| | - Amir Sharafkhaneh
- Sleep Center, Michael E. DeBakey, Veterans Affairs Medical Center, Houston, Texas
| | - Bijan Najafi
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Department of Surgery, Baylor College of Medicine, Houston, Texas
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