ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Abstract

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.

Contribution of EEG biosignals for Stress Detection

“…Other studies in stress detection have focused on the combination of multiple bio-signals. Hemakom et al [29] used EEG and ECG signals, and Zontone et al [30] proposed a method that combines EDA and ECG signal analysis to detect stress in car drivers. Similarly, Affani [31] introduced a stress detection design with two EDA sensors and two ECG sensors, exhibiting high-level performances in terms of linearity and jitter during metrological characterization.…”

“…While we utilize 38 features, the methodologies of other studies include a substantially smaller set: 5 features in [27] and [31], 9 in [30], and 10 in [28]. Notably, [29] utilized a much larger feature set (147 features) derived from only 8 EEG channels. While a larger number of features can potentially capture a wider range of information, it also increases the risk of including redundant or irrelevant features.…”

Psychological Stress Classification Using EEG and ECG: A CNN Based Multimodal Fusion Model

“…Gonzalez-Carabarin et al [16] investigated the effective features of EEG and ECG signals for stress classi cation respectively, reporting the most relevant EEG channels for different stress levels and the availability of HRV for stress analysis. Hemakom et al [17] con rmed that multimodal approaches are more reliable compared to unimodal approaches by extracting features with high EEG and ECG correlations to input into a traditional machine learning model for stress classi cation. However, these methods rely on manual feature extraction, which may lead to the loss of key features, and it is timeconsuming and laborious to try to nd effective features, while deep learning multimodal fusion methods based on the stress classi cation task have rarely been studied.…”