Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.
Authors
Ghosh, Abhirup
Puthusseryppady, Vaisakh
Chan, Dennis
Mascolo, Cecilia
Hornberger, Michael
Publication Date
2022-02-24Journal Title
Sci Rep
ISSN
2045-2322
Publisher
Springer Science and Business Media LLC
Volume
12
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Ghosh, A., Puthusseryppady, V., Chan, D., Mascolo, C., & Hornberger, M. (2022). Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.. Sci Rep, 12 (1) https://doi.org/10.1038/s41598-022-06899-w
Abstract
Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value [Formula: see text]), and distance from home (p-value [Formula: see text]). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.
Keywords
Article, /692/53/2421, /631/378/1689/1283, /639/705/1046, article
Sponsorship
Wellcome Trust (213939)
the Earle & Stuart Charitable Trust and the Faculty of Medicine and Health Sciences, University of East Anglia (R205319)
Identifiers
s41598-022-06899-w, 6899
External DOI: https://doi.org/10.1038/s41598-022-06899-w
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334417
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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