Neural Signal Processing Research Team, National Electronics and Computer Technology Center, National Science and Technology Development Agency, Pathumthani, Thailand.
Division of Health and Applied Sciences, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand.
PLoS One. 2023 Sep 1;18(9):e0291070. doi: 10.1371/journal.pone.0291070. eCollection 2023.
Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.
心理健康，尤其是压力，对生活质量起着至关重要的作用。在月经周期的不同阶段（黄体期和卵泡期），女性对压力的反应可能与男性不同。因此，如果不考虑性别，这可能会对机器学习模型的压力检测和分类准确性产生影响。然而，这一点以前从未被研究过。此外，只有少数几种压力检测设备经过了科学验证。为此，这项工作通过 ECG 和 EEG 信号，针对未指定和指定性别，提出了压力检测和多层次压力分类模型。通过开发和评估多个单一分类器，实现了压力检测模型。另一方面，采用堆叠技术获得多层次压力分类模型。使用来自 40 名受试者（21 名女性和 19 名男性）的 ECG 和 EEG 特征来训练和验证模型。在低&高综合压力条件下，RBF-SVM 和 kNN 分别为女性（79.81%）和男性（73.77%）提供了最高的平均分类准确率。结合 ECG 和 EEG，平均分类准确率至少提高到 87.58%（男性，高压力）和 92.70%（女性，高压力）。对于来自 ECG 和 EEG 的多层次压力分类，女性的准确率为 62.60%，男性的准确率为 71.57%。这项研究表明，性别差异会影响压力的检测和多层次分类的分类性能。开发的模型可用于个人（通过 ECG）和临床（通过 ECG 和 EEG）压力监测，无论是否考虑性别。
