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Early Detection of Dementia: Advances, Challenges, and Future Prospects
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Author(s): Stefanos Xefteris (Aristotle University of Thessaloniki, Greece), Evdokimos Konstantinidis (Aristotle University of Thessaloniki, Greece), Antonis S. Billis (Aristotle University of Thessaloniki, Greece), Panagiotis E. Antoniou (Aristotle University of Thessaloniki, Greece), Charis Styliadis (Aristotle University of Thessaloniki, Greece), Evangelos Paraskevopoulos (Aristotle University of Thessaloniki, Greece), Panagiotis Emmanouil Kartsidis (Aristotle University of Thessaloniki, Greece), Christos A. Frantzidιs (Aristotle University of Thessaloniki, Greece)and Panagiotis D. Bamidis (Aristotle University of Thessaloniki, Greece)
Copyright: 2020
Pages: 26
Source title:
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch098
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Abstract
Early detection and prediction of dementia through unobtrusive techniques or obtrusive tests is still in exploratory status and despite the increase of interest in recent years, many challenges remain open in designing methodologies that can accurately predict its onset. This chapter addresses the problem of the early detection of dementia from two points of view: Detection based on unobtrusive paradigms both in lab and home environments (behavioral monitoring, serious games, home based assisted living applications in telemedicine) and detection based on neuroimaging approaches. The chapter also provides information on setting up ecologically valid home labs for dementia related experiments. Consequently, the aim of this chapter is to provide an overview of a complete methodology of how researchers can possibly detect or predict the onset of dementia through the current state-of-the-art, underline open challenges and illustrate future work in the field.
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