Key result
Gender-specific machine learning combining ECG and EEG achieves up to ~93% stress detection accuracy.
Why the study?
Gender differences in stress responses during menstrual cycle phases may impact the accuracy of machine learning models for stress detection, which has not been previously investigated.
Does gender-specific machine learning using ECG and EEG signals improve stress detection and classification accuracy in human subjects?
Does gender-specific machine learning using ECG and EEG signals improve stress detection and classification accuracy in human subjects?
Gender differences influence the performance of machine learning models for stress detection and classification using ECG and EEG signals.
Gender-specific ECG-EEG models may refine stress detection; leaves open prospective clinical validation before practice change.
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.
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Atiwiwat et al. (2023) studied Mental Stress (n=40). ECG and EEG based machine learning models was evaluated on Classification accuracy for stress detection. Combining ECG and EEG features in machine learning models achieved stress classification accuracies of up to 92.70% for females and 87.58% for males, demonstrating that gender-specific models improve stress detection.
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