Department of Computer Science, King Saud University, Riyadh, Saudi Arabia.
Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
PLoS One. 2022 Mar 18;17(3):e0265679. doi: 10.1371/journal.pone.0265679. eCollection 2022.
Human anxiety is a grave mental health concern that needs to be addressed in the appropriate manner in order to develop a healthy society. In this study, an objective human anxiety assessment framework is developed by using physiological signals of electroencephalography (EEG) and recorded in response to exposure therapy. The EEG signals of twenty-three subjects from an existing database called "A Database for Anxious States which is based on a Psychological Stimulation (DASPS)" are used for anxiety quantification into two and four levels. The EEG signals are pre-processed using appropriate noise filtering techniques to remove unwanted ocular and muscular artifacts. Channel selection is performed to select the significantly different electrodes using statistical analysis techniques for binary and four-level classification of human anxiety, respectively. Features are extracted from the data of selected EEG channels in the frequency domain. Frequency band selection is applied to select the appropriate combination of EEG frequency bands, which in this study are theta and beta bands. Feature selection is applied to the features of the selected EEG frequency bands. Finally, the selected subset of features from the appropriate frequency bands of the statistically significant EEG channels were classified using multiple machine learning algorithms. An accuracy of 94.90% and 92.74% is attained for two and four-level anxiety classification using a random forest classifier with 9 and 10 features, respectively. The proposed state anxiety classification framework outperforms the existing anxiety detection framework in terms of accuracy with a smaller number of features which reduces the computational complexity of the algorithm.
人类焦虑是一个严重的心理健康问题，需要以适当的方式加以解决，以建立一个健康的社会。在这项研究中，通过使用基于心理刺激的焦虑状态数据库（DASPS）中记录的脑电图（EEG）生理信号，开发了一种客观的人类焦虑评估框架。该研究使用了来自现有的名为“基于心理刺激的焦虑状态数据库（DASPS）”的数据库中的 23 名被试的 EEG 信号，将焦虑程度分为两个和四个等级进行量化。使用适当的噪声滤波技术对 EEG 信号进行预处理，以去除不需要的眼动和肌肉伪影。使用统计分析技术对 EEG 信号进行通道选择，以选择具有显著差异的电极，分别用于二进制和四级分类的人类焦虑。从选定 EEG 通道的数据中提取特征。应用频带选择从 EEG 频带中选择合适的组合，在本研究中，选择了 theta 和 beta 频带。对选定 EEG 频带的特征进行特征选择。最后，使用多种机器学习算法对从具有统计学意义的 EEG 通道的适当频带中选择的特征子集进行分类。使用随机森林分类器，对于两级和四级焦虑分类，分别使用 9 个和 10 个特征，可分别达到 94.90%和 92.74%的准确率。与现有的焦虑检测框架相比，该提出的状态焦虑分类框架在准确性方面表现更好，具有更少的特征，从而降低了算法的计算复杂度。
