Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.
Springer Science and Business Media LLC
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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
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.
Article, /692/53/2421, /631/378/1689/1283, /639/705/1046, article
Wellcome Trust (213939)
the Earle & Stuart Charitable Trust and the Faculty of Medicine and Health Sciences, University of East Anglia (R205319)
External DOI: https://doi.org/10.1038/s41598-022-06899-w
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334417