Department of Computer Science and Engineering, School of Engineering and Technology, Jamia Hamdard, Delhi 110062, India.
Comput Intell Neurosci. 2020 Aug 1;2020:8860841. doi: 10.1155/2020/8860841. eCollection 2020.
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 1 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.
压力被归类为由于令人不安或有要求的情况而导致的精神紧张或压力的状态。压力的产生有多种来源。研究人员认为人类大脑是压力的主要来源。为了研究每个人如何以不同的形式遇到压力，研究人员进行了调查并进行了监测。本文提出了融合 5 种算法的方法，以使用 EEG 信号提高精神压力检测的准确性。已经对鲸鱼优化算法进行了修改，以在 SVM 分类器中选择最佳核函数进行压力检测。还使用了一组集成的算法（NLM、DCT 和 MBPSO）进行预处理、特征提取和选择。已经在从 14 位受试者收集的 EEG 信号上测试了所提出的算法，以识别压力水平。该方法使用准确率、敏感度、特异性和 1 分数进行了验证，准确率、敏感度、特异性和 1 分数分别为 96.36%、96.84%、90.8%和 97.96%，优于现有方法。该算法对于精神科医生和健康顾问诊断压力水平可能有用。
