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Lifelog-based classification of mild cognitive impairment using artificial neural networks

Title
Lifelog-based classification of mild cognitive impairment using artificial neural networks
Authors
Lee, Sang-HoKang, Won-SeokMoon, Cheil
DGIST Authors
Kang, Won-SeokMoon, Cheil
Issue Date
2018-01-24
Citation
17th International Conference on Electronics, Information and Communication, ICEIC 2018, 1-2
Type
Conference
ISBN
9781538647547
Abstract
The swift diagnosis and treatment of mild cognitive impairment (MCI), as a prestage of dementia, are important to reduce the enormous costs of dementia treatment. The aim of this paper is to investigate the potential features in human behavior to facilitate the early diagnosis of MCI. In order to extract specific features from lifelogs, we collected data of activity and sleep using Fitbit's wrist band worn day and night from 12 subjects, for 12 week each. These data were analyzed using the SPSS (Statistical Package for Social Science) for verification and 12 total numbers of the significant features are extracted, further these features used for classification based on artificial neural networks (ANNs). ANNs with 8 input neurons (extracted features), 4 hidden neurons, and output neurons (diagnosis) were used to classify the patients. The results indicate that lifelog-based classifier have a good capacity (AUC=0.81 ±0.08) to discriminate MCI patients from healthy controls. © 2018 Institute of Electronics and Information Engineers.
URI
http://hdl.handle.net/20.500.11750/6673
DOI
10.23919/ELINFOCOM.2018.8330611
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Moon, Cheil Laboratory of Chemical Senses
  • Research Interests Brain convergent science based on chemical senses; olfaction; 감각신경계 기반 뇌융합과학; 후각 신경계
Files:
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Collection:
Convergence Research Center for Wellness2. Conference Papers
Department of Brain and Cognitive SciencesLaboratory of Chemical Senses2. Conference Papers


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