An algorithm for actigraphy-based sleep/wake scoring: Comparison with polysomnography
| Autor: | Wiebke Hermann, Thomas Kirste, Heike Benes, Stefan Lüdtke, Stefan J. Teipel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Sleep Wake Disorders
Male diagnosis [Sleep Wake Disorders] Sleep wake Polysomnography Hidden Markov Model 050105 experimental psychology Cross-validation 03 medical and health sciences Sleep/wake scoring 0302 clinical medicine Physiology (medical) medicine Humans 0501 psychology and cognitive sciences ddc:610 Wakefulness Hidden Markov model Aged physiopathology [Sleep Wake Disorders] medicine.diagnostic_test physiology [Wakefulness] business.industry 05 social sciences Actigraphy Gold standard (test) Sleep architecture physiology [Sleep] Sensory Systems Neurology Female Neurology (clinical) Sleep (system call) business Sleep Algorithm psychological phenomena and processes 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Clinical neurophysiology 132(1), 137-145 (2021). doi:10.1016/j.clinph.2020.10.019 |
| DOI: | 10.1016/j.clinph.2020.10.019 |
| Popis: | Objective To evaluate the accuracy of actigraphy against polysomnography (PSG) as gold standard using a newly developed algorithm for sleep/wake discrimination that explicitly models the temporal structure of sleep. Methods PSG was recorded in 11 men and 9 women (age 71.1 ± 5.0 ) to evaluate suspected neuropsychiatric sleep disturbances. Simultaneously, wrist actigraphy was recorded, from which 37 features were computed for each 1-min epoch. We compared prediction of PSG-derived sleep/wake states for each of these features between our newly developed algorithm, and four state-of-the-art algorithms. The algorithms were evaluated using a leave-one-subject out cross validation. Results The new algorithm classified 84.9% of sleep epochs (sensitivity) and 74.2% of wake epochs correctly (specificity), leading to a sleep/wake scoring accuracy of 79.0%. Four out of five sleep parameters were estimated more accurately by the new algorithm than by state-of-the-art algorithms. Conclusion The proposed algorithm achieved a significantly higher specificity than state-of-the-art algorithm, with only minor decrease in sensitivity for patients with sleep disorders. We assume this reflects the capability of the algorithm to explicitly model sleep architecture. Significance The unobtrusive assessment of sleep/wake cycles is particularly relevant for patients with neuropsychiatric diseases that are associated with sleep disturbances, such as depression or dementia. |
| Databáze: | OpenAIRE |
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