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

Sang Ho Lee; Won Seok Kang; Cheil Moon

doi:10.23919/ELINFOCOM.2018.8330611

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

Sang Ho Lee, Won Seok Kang, Cheil Moon

Daegu Gyeongbuk Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	International Conference on Electronics, Information and Communication, ICEIC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-2
Number of pages	2
ISBN (Electronic)	9781538647547
DOIs	https://doi.org/10.23919/ELINFOCOM.2018.8330611
State	Published - 2 Apr 2018
Event	17th International Conference on Electronics, Information and Communication, ICEIC 2018 - Honolulu, United States Duration: 24 Jan 2018 → 27 Jan 2018

Publication series

Name	International Conference on Electronics, Information and Communication, ICEIC 2018
Volume	2018-January

Conference

Conference	17th International Conference on Electronics, Information and Communication, ICEIC 2018
Country/Territory	United States
City	Honolulu
Period	24/01/18 → 27/01/18

Bibliographical note

Publisher Copyright:
© 2018 Institute of Electronics and Information Engineers.

Keywords

ANNs
Classification
MCI
Machine learning
Wearable device

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.23919/ELINFOCOM.2018.8330611

Cite this

Lee, S. H., Kang, W. S., & Moon, C. (2018). Lifelog-based classification of mild cognitive impairment using artificial neural networks. In International Conference on Electronics, Information and Communication, ICEIC 2018 (pp. 1-2). (International Conference on Electronics, Information and Communication, ICEIC 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ELINFOCOM.2018.8330611

Lee, Sang Ho ; Kang, Won Seok ; Moon, Cheil. / Lifelog-based classification of mild cognitive impairment using artificial neural networks. International Conference on Electronics, Information and Communication, ICEIC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-2 (International Conference on Electronics, Information and Communication, ICEIC 2018).

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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.",

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Lee, SH, Kang, WS & Moon, C 2018, Lifelog-based classification of mild cognitive impairment using artificial neural networks. in International Conference on Electronics, Information and Communication, ICEIC 2018. International Conference on Electronics, Information and Communication, ICEIC 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-2, 17th International Conference on Electronics, Information and Communication, ICEIC 2018, Honolulu, United States, 24/01/18. https://doi.org/10.23919/ELINFOCOM.2018.8330611

Lifelog-based classification of mild cognitive impairment using artificial neural networks. / Lee, Sang Ho; Kang, Won Seok ; Moon, Cheil.
International Conference on Electronics, Information and Communication, ICEIC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-2 (International Conference on Electronics, Information and Communication, ICEIC 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Lee SH, Kang WS , Moon C. Lifelog-based classification of mild cognitive impairment using artificial neural networks. In International Conference on Electronics, Information and Communication, ICEIC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-2. (International Conference on Electronics, Information and Communication, ICEIC 2018). doi: 10.23919/ELINFOCOM.2018.8330611

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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