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Machine learning based classification of prefrontal cortex activation based on severity of Alzheimer’s disease with portable fNIRS device

Title
Machine learning based classification of prefrontal cortex activation based on severity of Alzheimer’s disease with portable fNIRS device
Authors
Kim, Bomin
DGIST Authors
Kim, Bomin; Choi, Ji-WoongMoon, Cheil
Advisor(s)
최지웅
Co-Advisor(s)
Moon, Cheil
Issue Date
2020
Available Date
2020-08-06
Degree Date
2020/08
Type
Thesis
Description
fNIRS, Alzheimer's disease, Machine learning, Artificial neural network (ANN), Prefrontal cortex, Functional connectivity
Abstract
Alzheimer’s disease (AD) is an infamous degenerative brain disease accompanied with severe cognitive decline. By the average life expectancy of humanity increasing, the number of potential and actual patients of AD has been rising. Still a fundamental treatment of AD is lacking, medication which can delay the degradation of cognition is available. For patients’ and their caregivers’ well-being, early detection of AD, which makes achievable of fast medication slowing down the cognitive decline, is needed. This study gives the positive feasibility of simple and easily accessible AD diagnosis using portable fNIRS device which can cover the prefrontal cortex of user’s head. With participants undergoing working memory (WM) based task, we observed the distinctive prefrontal cortices’ activation by detecting amount of oxy-hemoglobin (HBO) and deoxy-hemoglobin (HBR). Collected data were analyzed by functional connectivity analysis. By machine learning based classification, we were able to find the possibility of diagnosing people by prefrontal cortex observation with mild cognitive impairment and AD from healthy people, which normally done by using data from magnetic resonance imaging (MRI) and positron emission tomography (PET).
Table Of Contents
Ⅰ. Introduction ··· ··················································································· 1 II. Methods and materials 2.1 Participants ··················································································· 6 2.2 Working memory task based experiments ··············································· 6 2.3 Portable fNIRS device ······································································ 8 III. Data analysis and classification 3.1 The modified Beer-Lambert law ························································ 10 3.2 Signal preprocessing ······································································ 11 3.3 Functional connectivity ·································································· 11 3.4 Machine learning based classification ·········································· 13 VI. Discussion ··················································································· 23 V. Conclusion ··················································································· 24 References ··················································································· 25
URI
http://dgist.dcollection.net/common/orgView/200000322121
http://hdl.handle.net/20.500.11750/12164
DOI
https://doi.org/10.22677/thesis.200000322121
Degree
Master
Department
Department of Information and Communication Engineering
University
DGIST
Related Researcher
  • Author Moon, Cheil Laboratory of Chemical Senses
  • Research Interests Brain convergent science based on chemical senses; olfaction; 감각신경계 기반 뇌융합과학; 후각 신경계
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Collection:
Department of Information and Communication EngineeringThesesMaster


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