Wearable sleep recording augmented by artificial intelligence for Alzheimer's disease screening.

Journal: npj aging
Published Date: May 9, 2025

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.

Authors

  • Elisabeth R M Heremans
    STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics-Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium.
  • Astrid Devulder
    Laboratory for Epilepsy Research, KU Leuven Biomedical Sciences Group, Leuven, Belgium.
  • Pascal Borzée
    Department of Pulmonary Diseases, University Hospitals Leuven, Leuven, Belgium.
  • Rik Vandenberghe
    Department of Neurology, University Hospitals Leuven, Leuven, Belgium.
  • François-Laurent De Winter
    Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven Biomedical Sciences Group, Leuven, Belgium.
  • Mathieu Vandenbulcke
    Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven Biomedical Sciences Group, Leuven, Belgium.
  • Maarten Van Den Bossche
    Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven Biomedical Sciences Group, Leuven, Belgium.
  • Bertien Buyse
    Department of Pulmonary Diseases, University Hospitals Leuven, Leuven, Belgium.
  • Dries Testelmans
    Department of Pulmonary Diseases, University Hospitals Leuven, Leuven, Belgium.
  • Wim Van Paesschen
    Laboratory for Epilepsy Research, KU Leuven Biomedical Sciences Group, Leuven, Belgium.
  • Maarten De Vos
    STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics-Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium. maarten.devos@kuleuven.be.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals