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Dynamic-Time-Warping Analysis of Feature-Vector Reliability for Cognitive Stimulation Therapy Assessment
Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu.ORCID iD: 0000-0002-4255-5130
2015 (English)In: Information Science and Applications / [ed] Kuinam J. Kim, Springer Berlin/Heidelberg, 2015, 235-241 p.Chapter in book (Other academic)Text
Abstract [en]

Cognitive stimulation therapy (CST) can help people with mental illness improve their health condition. In particular, CST provides an alternative treatment for people with mild to moderate dementia. Signal processing and pattern recognition methods are promising tools for automated assessment of the effectiveness of CST in treating individuals with dementia. This paper applies the dynamic time-warping for investigating the reliability of photoplethysmography-derived features extracted by the largest Lyapunov exponents and spectral distortion for CST evaluation.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. 235-241 p.
Series
, Lecture Notes in Electrical Engineering, ISSN 1876-1100 ; 339
Keyword [en]
Dynamic time-warping, Largest lyapunov exponents, Spectral distortion, Dementia, Cognitive stimulation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-128584DOI: 10.1007/978-3-662-46578-3_27ISBN: 978-3-662-46577-6ISBN: 978-3-662-46578-3OAI: oai:DiVA.org:liu-128584DiVA: diva2:931183
Available from: 2016-05-26 Created: 2016-05-25 Last updated: 2016-06-13

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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ReferencesLink to record
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