By Mohammad Haroon Ahmed, John Panchookian, Michael Grillo, Yasith Weerasinghe, Amirtaha Taebi, Fadil Qadri, Peshala Gamage, Mehmet Kaya
Abstract
Stress has significant effects on health, yet there is limited research on effective methods for quantifying stress detection. Monitoring physiological changes presents a promising approach to stress management. This study compares the effectiveness of electroencephalography (EEG), electrocardiography (ECG)-derived heart rate variability (HRV), and trapezius muscle electromyography (EMG) in stress classification. Sixteen healthy participants (ages 18–46) completed three sessions in a controlled environment. Baseline activity was compared to stress-induced changes during a Stroop color word test and mental arithmetic task. EEG, HRV, and EMG features were analyzed in 30-second intervals to assess their ability to detect stress. EEG features were found to be the most effective, followed by HRV and EMG. Machine learning techniques (SVM, KNN, neural network, and random forest) were applied for subject-specific classification. EEG achieved the highest accuracy (86.45 ± 7.22%), while HRV and EMG yielded similar accuracies (77.36 ± 9.10% and 81.84 ± 6.13%, respectively). When combining HRV and EMG features, an accuracy of 87.51 ± 7.18% was achieved, comparable to EEG. These findings suggest that wearable sensors utilizing EMG and HRV could effectively detect stress without the need for EEG. This approach could open up new avenues for stress management in real-world settings. Future studies with larger sample sizes will work towards developing a universal stress classification model.
