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Detection of Chronic Stress using Bio-Signals

Affiliations

  • Department of Computer Science and Engineering, Kyungnam University, 7 Kyungnamdaehak-ro,Masanhappo-gu, Changwon-si, Gyeongsangnam-do - 51767, Korea, Republic of

Abstract


Objectives: This research is used to detect chronic stress using EEG (Electroencephalogram) and ECG (Electrocardiogram). Methods/Statistical Analysis: We collected and analyzed salivary cortisol as index of the endocrine response to stress. We measured and analyzed EEG and ECG under various conditions. Thirty-three, right-handed subjects participated in the test. They who are on the brew were all between 30 and 40 years (female 9, male 24). We used good and bad images supporting by IAPS to induce emotion. Findings: The higher stress level shows lower all HRV features. HRV features according to stimuli were little changed in range of variance. Those who have lower HRV SDNN show EEG high beta activity at positions such as FC5 and FC6 than those who have not. Those who have higher salivary cortisol present higher EEG high beta activity. It appears that EEG, ECG, and salivary cortisol are closely related to chronic psychological stress. Also, there is individual’s difference of stress level based on HRV SDNN and salivary cortisol. Stress level got higher when HRV SDNN decreases or cortisol increases. The higher group of stress level shows higher EEG high beta activity while subjects close their eyes. However, there is no significant correlation among EEG, ECG, and salivary cortisol during eyes-open task. Improvements/Applications: EEGhigh beta power at anterior temporal area plays a role as risk factor for chronic psychological stress. It serves as a diathesis for dysphoria related chronic psychological stress.

Keywords

Bio-Signal Processing, Chronic Stress, EEG, ECG, Salivary Cortisol.

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