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

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 stress detection and classification
accuracy of machine learning models that 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, 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 condition, 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.