Design of Healthcare Assistant Using EEG Signals for Stress

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Author(s): Nilima Salankar (University of Petroleum and Energy Studies, India)
Copyright: 2020
Pages: 10
Source title: Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Source Author(s)/Editor(s): Dilip Singh Sisodia (National Institute of Technology, Raipur, India), Ram Bilas Pachori (Indian Institute of Technology, Indore, India)and Lalit Garg (University of Malta, Malta)
DOI: 10.4018/978-1-7998-2120-5.ch006

Keywords: Artificial Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

Stress is a matter of concern for every age group. In this chapter, the author has focused on the age group 17-24 (five males and five females), who have just completed senior secondary and entered into an undergraduate course, who are vulnerable to cope up with the situation of transition. The focus of the chapter is on the design of the study protocol to identify stress levels by using induced stress tests like Stroop color test and arithmetic three-level (easy, medium, and hard) test. Data is divided into bands delta theta alpha beta and gamma by using four levels of discrete wavelet transform, and features extracted are Hjorth parameters activity, mobility, and complexity from the combination of channels and band levels. The accuracy achieved for stress and no-stress for male vs. male, female vs. female, and male vs. female is in the range 93-98% for the combination of channels at the frontal position and at band delta theta alpha beta and gamma whereas less involvement has been observed for other lobes parietal, temporal, and occipital.

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Design of Healthcare Assistant Using EEG Signals for Stress

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