1014 Sleep Monitoring in Mild Cognitive Impairment Using Noninvasive, Under the Sheet Sensors
Bibliographic record
Abstract
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. 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. 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). 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. N/A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".