Modified Support Vector Machine for Detecting Stress Level Using EEG Signals.

Abstract:

:Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms to enhance the accuracy for detection of mental stress using EEG signals. The Whale Optimization Algorithm has been modified to select the optimal kernel in the SVM classifier for stress detection. An integrated set of algorithms (NLM, DCT, and MBPSO) has been used for preprocessing, feature extraction, and selection. The proposed algorithm has been tested on EEG signals collected from 14 subjects to identify the stress level. The proposed approach was validated using accuracy, sensitivity, specificity, and F1 score with values of 96.36%, 96.84%, 90.8%, and 97.96% and was found to be better than the existing ones. The algorithm may be useful to psychiatrists and health consultants for diagnosing the stress level.

journal_name

Comput Intell Neurosci

authors

Gupta R,Alam MA,Agarwal P

doi

10.1155/2020/8860841

subject

Has Abstract

pub_date

2020-08-01 00:00:00

pages

8860841

eissn

1687-5265

issn

1687-5273

journal_volume

2020

pub_type

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