Identifying neurophysiological correlates of stress - Pei, Dingyi; Tirumala, Shravika; Tun, Kyaw T.; Ajendla, Akshara; Vinjamuri, Ramana (Frontiers in Medical Engineering, 2024)
Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting well-being and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-second neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications.