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Prediction of Mild Cognitive Impairment Using Movement Complexity
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. (HMC2)ORCID iD: 0000-0003-0878-8130
Artificial Intelligence for Medical Systems Lab, Oregon Health and Science University, Oregon, USA.
2021 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 25, no 1, p. 227-236Article in journal (Refereed) Published
Abstract [en]

Objective: Aimless movement or wandering may be a symptom of mild cognitive impairment (MCI) that arises as a consequence of confusion and forgetfulness. This paper presents a support vector machine (SVM) framework based on movement analysis for the prediction of the onset and progression of MCI.

Methods: Movement data of 22 subjects with MCI, and 22 other healthy subjects, living independently in smart homes were collected for ten years using motion sensors. Features were extracted from the sensor data using movement metrics, including cyclomatic complexity, detrended fluctuation analysis, fractal index, entropy, and room transitions. Two different SVM classification algorithms were trained using the features, first to predict the progression of MCI in the post-transition period, and second to predict the onset of MCI in the pre-transition phase.

Results: The two SVMs were able to detect the onset six months earlier than the clinical diagnosis. The model accuracy in classifying MCI increased monotonically from the onset month and reached maximum (81%) at the 11th post-transition month. The features of cyclomatic complexity contributed significantly to the prediction results.

Conclusion: Findings support the use of movement complexity measures and machine learning for monitoring cognitive behavior in an independent living environment.

© Copyright 2020 IEEE., All rights reserved.

Place, publisher, year, edition, pages
Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2021. Vol. 25, no 1, p. 227-236
Keywords [en]
Cyclomatic complexity, mild cognitive impairment, movement analysis, support vector machines
National Category
Neurology Neurosciences
Identifiers
URN: urn:nbn:se:hh:diva-43766DOI: 10.1109/jbhi.2020.2985907ISI: 000641705100023PubMedID: 32287025Scopus ID: 2-s2.0-85098191691OAI: oai:DiVA.org:hh-43766DiVA, id: diva2:1515834
Projects
HMC2
Funder
NIH (National Institute of Health)
Note

In the article, there is an error in [13] and its in-text citations. The reference should appear as below:

[13] W. D. Kearns, V. O. Nams, and J. L. Fozard, “Tortuosity in movement paths is related to cognitive impairment. Wireless fractal estimation in assisted living facility residents,” Methods Inf. Med. vol. 49, no. 6, pp. 592–598, 2010, doi: 10.3414/ME09-01-0079.

The corrected citation in the body of the article is “Kearns et al.” [1, pp. 228, 230].

Available from: 2021-01-11 Created: 2021-01-11 Last updated: 2022-10-31Bibliographically approved

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