Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks.
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 mor...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 12; pp. 1 - 7 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110463852&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110463852 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2015 vid: 39 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110463852 110463852 110463852 10.1007/s10916-015-0345-9 NLM26490145 110463852 ppf: 1 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks. aug: au: Li, Xiaowei Hu, Bin Shen, Ji Xu, Tingting Retcliffe, Martyn affil: School of Information and Science & Engineering, Lanzhou University, Lanzhou China sug: subj: Depression Diagnosis Electroencephalography Students, College Depression Classification Human Descriptive Statistics Electrodes Wearable Sensors Logistic Regression Male Female China Clinical Assessment Tools ROC Curve Right Brain Hemisphere Left Brain Hemisphere Funding Source Male Female ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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