Articlenpj aging2025
Wearable sleep recording augmented by artificial intelligence for Alzheimer's disease screening.
Article in npj aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Smart Wearable EEG Devices: A Review of Lightweight, Multi-Sensor Systems for Sleep and Everyday Neurophysiology.Biosensors · 2026Review
- UK Biobank at 20 - a growing, global resource for dementia research.Nature reviews. Neurology · 2026Review
- From brain waves to heartbeats: Exploring the combined role of electroencephalography and heart-rate variability in Alzheimer's disease diagnosis and management.Journal of Alzheimer's disease : JAD · 2026Review
- Prototype-based sleep micro-structure learning for explainable and robust multimodal recognition of sleep-related conditions.Research square · 2026Article
- The use of electroencephalography in neurodegenerative disease and its utility in dementia.NPJ dementia · 2026Review
- Dementia Risk and Machine Learning-Derived Brain Age Index from Sleep Electroencephalography: A Pooled Cohort Analysis of Over 7,000 Individuals Across Five Community Cohorts.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The recent emergence of wearable devices will enable large scale remote brain monitoring. This study investigated whether multimodal wearable sleep recordings could help screening for Alzheimer's disease (AD). Measurements were acquired simultaneously from polysomnography and a wearable device, measuring electroencephalography (EEG) and accelerometry (ACM) in 67 elderly without cognitive symptoms and 35 AD patients. Sleep staging was performed using an AI model (SeqSleepNet), followed by feature extraction from hypnograms and physiological signals. Using these features, a multi-layer perceptron was trained for AD detection, with elastic net identifying key features. The wearable AD detection model achieved an accuracy of 0.90 (0.76 for prodromal AD). Single-channel EEG and ACM physiological features captured sufficient information for AD detection and outperformed the hypnogram features, highlighting these physiological features as promising discriminative markers for AD. We conclude that wearable sleep monitoring augmented by AI shows promise towards non-invasive screening for AD in the older population.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.