Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.
Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility o...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 1; pp. 125 - 137 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2018
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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=127330156&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127330156 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2018 vid: 56 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127330156 127330156 144120464 NLM29043535 127330156 10.1007/s11517-017-1733-8 NLM29043535 127330156 ppf: 125 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. aug: au: Al-shargie, Fares Tang, Tong Boon Badruddin, Nasreen Kiguchi, Masashi affil: Centre of Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia sug: subj: Stress, Psychological Diagnosis Statistics Electroencephalography ROC Curve Male Young Adult Electrodes Human Funding Source Male ab: Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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