Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks

Li, X. and Hu, B. and Shen, J. and Xu, T. and Ratcliffe, M. (2015) Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks. Journal of Medical Systems, 39 (12). ISSN 01485598 (ISSN)

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Abstract

Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying EEG features during free viewing task, an accuracy of 99.1%, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate depressed and non-depressed subjects. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Combined with wearable EEG collecting devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time. © 2015, Springer Science+Business Media New York.

Item Type: Article
Uncontrolled Keywords: Classification, Depression detection, Healthcare EEG, Non-linear feature, area under the curve, Article, clinical article, college student, correlation analysis, cost effectiveness analysis, depression, diagnostic accuracy, diagnostic test accuracy study, electroencephalogram, electroencephalograph, electroencephalograph electrode, female, free viewing task, human, k nearest neighbor, male, mental health care, neurologic examination, nonlinear system, patient monitoring, physician, receiver operating characteristic, risk factor
Subjects: C800 Psychology
Divisions: UoA Collections > UoA 04: Psychology, Psychiatry and Neuroscience
Faculty of Business, Law and Social Sciences > School of Social Sciences > Dept. Psychology
Depositing User: Jessica Baylis
Date Deposited: 10 Feb 2017 12:12
Last Modified: 10 Feb 2017 12:12
URI: http://www.open-access.bcu.ac.uk/id/eprint/562

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