Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
| Autor: | Poyiadzi, R., Yang, W., Ben-Shlomo, Y., Craddock, I., Coulthard, L., Raul Santos-Rodriguez, Selwood, J., Twomey, N. |
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| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Computers and Society Computer Science - Machine Learning Statistics - Machine Learning Computers and Society (cs.CY) FOS: Electrical engineering electronic engineering information engineering Machine Learning (stat.ML) Electrical Engineering and Systems Science - Signal Processing Machine Learning (cs.LG) |
| Zdroj: | Scopus-Elsevier |
| DOI: | 10.48550/arxiv.2007.03615 |
| Popis: | There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the \textit{early} stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset of longitudinal sensor data from persons with MCI and AD. We present preliminary findings that show the relationship between levels of sleep quality and wandering can be subtly different between patients in the early stages of dementia and healthy cohabiting controls. Comment: Accepted by the 1st edition of HELPLINE: Artificial Intelligence for Health, Personalized Medicine and Wellbeing |
| Databáze: | OpenAIRE |
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