Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data
| العنوان: | Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data |
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| المؤلفون: | Kenneth R. Diller, Shahab Haghayegh, Richard J. Castriotta, Michael H. Smolensky, Sepideh Khoshnevis |
| المصدر: | Chronobiology International. 36:1752-1760 |
| بيانات النشر: | Informa UK Limited, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Adult, Male, Adolescent, Physiology, Computer science, 030209 endocrinology & metabolism, Actigraphy, Wrist, Circadian Rhythm, Young Adult, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Physiology (medical), Performance comparison, medicine, Humans, Female, Sleep (system call), Sleep, Algorithm, Algorithms, 030217 neurology & neurosurgery |
| الوصف: | We compared performance of four popular interpretative algorithms (IAs), i.e., Cole-Kripke, Rescored Cole-Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of 'Rescoring Rules' improved performance of the Cole-Kripke IA. The Sadeh and Rescored Cole-Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen's Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen's kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention. |
| تدمد: | 1525-6073 0742-0528 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45c2b312ed479b066f4567e393205c0f https://doi.org/10.1080/07420528.2019.1679826 |
| رقم الأكسشن: | edsair.doi.dedup.....45c2b312ed479b066f4567e393205c0f |
| قاعدة البيانات: | OpenAIRE |
PDF full text from Publisher | تدمد: | 15256073 07420528 |
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