1014 Sleep Monitoring in Mild Cognitive Impairment Using Noninvasive, Under the Sheet Sensors. (27th April 2018)

• Record Type: Journal Article
• Authors: Almaghasilah, A; Daigle, K; Gilbert, C; Sulinski, E; Aronis, J; Bouchard, A; Delp, T; Singer, C; Abedi, A; Hayes, M
• Is Part Of: Sleep. Volume 41(2018)Supplement 1
• Page Start: A376
• Page End: A376
• Publication Date: 2018-04-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsy061.1013
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Sleep disorders (SD) are common in people with Mild Cognitive Impairment (MCI) from early Alzheimer's disease (AD). This study investigated whether a new, inexpensive pressure-sensing technology of sleep monitoring could differentiate MCI patients from age-matched, cognitively normal controls. Methods: MCI-diagnosed (n=10) and age-matched controls with normal cognition (NC) (n=10), 65–85 years of age, recruited from a memory disorder clinic and community respectively, were studied in the home (2 nights) with a flat-sheet mattress pressure-sensor device and standard wrist actigraphy (7 nights). Resistive sensor technology, signal processing and statistical inference identified 2 distinct signature biomarkers of SD: high frequency (2–5'), low amplitude movement arousals (MAs) (not measured with standard actigraphy), and respiratory rate (RR). MA and spontaneous movement (SM)-RR coupling; a time series segmentation analysis of respiratory variability linked to MAs. Flat-sheet pressure sensor device-based sleep-wake and full arousals were based on the Kripke algorithm and compared to standard actigraphy. Results: Standard actigraphy algorithm sleep scoring did not discriminate MCI from NC on any sleep variable although immobility trended higher in MCI (p<.06). SM-RR coupling index was significantly lower in MCI relative to NC group (M=0.51, SD=.07 vs. M=0.83, SD=.05) using General Linear Modeling with age as covariate (p<.02). Independent of group, lower SM-RR coupling index predicted higher self-reported sleepiness (p<.02) on the Stanford Sleepiness Scale (SSS) and device sleep fragmentation measure correlated positively with SSS average (r=0.49, p<.01). Conclusion: Home-monitoring in MCI revealed abnormal SM-RR coupling in MCI not detected by standard actigraphy. MA and sleep-related autonomic function (RR) time series suggest a neuroprotective function for this arousal neurocircuity to support respiratory tone, airway patency, and moderate hypoxia during sleep that is impaired in MCI relative to NC and may detect early cognitive decline. Support (If Any): N/A.