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 sta

SUBMITTER: Hemakom A 

PROVIDER: S-EPMC10473514 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature