Cited By
View all- Gooding PKariotis T(2021)Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping ReviewJMIR Mental Health10.2196/246688:6(e24668)Online publication date: 10-Jun-2021
A depressive mood status quantitative reasoning method based on portable EEG and self-rating scale
Authors: 
Zhijiang Wan, Ning Zhong, Jianhui Chen, Haiyan Zhou, Jie Yang, Jianzuo YanAuthors Info & Claims
Zhijiang Wan, Ning Zhong, Jianhui Chen, Haiyan Zhou, Jie Yang, Jianzuo YanAuthors Info & ClaimsPages 389 - 395
Abstract
In order to actualize the quantitative reasoning function into the portable brain and mental health-monitoring system under WaaS architecture, this study proposes a method named quantitative reasoning for depressive mood status based on portable EEG and self-rating scale data. 5 inpatients were recruited to join the experiment, from which the portable EEG data and clinical self-rating scale data of 2 weeks were collected. The principal component analysis method is adopted to process the self-rating data. The regression analysis based on random forest algorithm is used to generate the quantitative reasoning model for acquiring reasoning rules. In order to further implement the quantitative reasoning function, the Protege and Jena are adopted to build data ontologies and actualize an automatic reasoning function for objectively quantifying the depressive mood status respectively. The effectiveness of reasoning rules are validated, the preliminary results show that the expected quantitative value outputted from the quantitative reasoning model is highly correlated (the absolute value of correlation coefficient ≥0.7, P-value ≤0.05) with the actual self-rating scale data.
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Published In
August 2017
1284 pages
ISBN:9781450349512
DOI:10.1145/3106426
- Conference Chair:
- Amit Sheth,
- General Chairs:
- Axel Ngonga,
- yin Wang,
- Elizabeth Chang,
- Dominik Ślęzak,
- Bogdan Franczyk,
- Program Chairs:
- Rainer Alt,
- Xiaohui Tao
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Published: 23 August 2017
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- National Natural Science Foundation of China
- National Basic Research Program of China
- Beijing Outstanding Talent Training Foundation
- Beijing Natural Science Foundation
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WI '17
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- SIGAI
- TCII
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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
Overall Acceptance Rate 118 of 178 submissions, 66%
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View all- Gooding PKariotis T(2021)Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping ReviewJMIR Mental Health10.2196/246688:6(e24668)Online publication date: 10-Jun-2021
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