Electrogastrogram: Demonstrating Feasibility in Mental Stress Assessment using Sensor Fusion

Abstract

This paper covers the feasibility of electrogastrogram (EGG) in multi-modal mental stress assessment in conjunction with electrocardiogram (ECG) and respiratory signal (RESP). In this study, twenty-one healthy participants were repeatedly relaxed, stressed, and highly stressed according to our experimental protocol, which was based on combined arithmetic and Stroop tasks, and their EGG, ECG, and RESP were simultaneously captured. Subsequently, various features were extracted from the signals, and correlation analysis was performed between mental stress levels and the features, especially the EGG features. Furthermore, conventional machine learning models were optimized and validated to verify the feasibility of EGG in mental stress detection. Some EGG features exhibited significant correlation to mental stress levels (ρbi,menaDP = -0.187 and ρmulti,meanDP = -0.177, p < 0.001). The correlation degree was comparable to that of the RESP features. The EGG features largely reflected individual differences regarding mental stress response compared to the ECG and RESP features. Most importantly, the utilization of the EGG features along with the ECG and RESP features significantly improved the accuracy of several models by up to 8% regarding mental stress detection. Especially, logistic regression exhibited moderate accuracy in detecting mental stress (70.15% accuracy and 0.741 area under the receiver operating characteristic curve). We believe that EGG monitoring could significantly contribute to in-depth mental stress evaluation, and potentially be used for the development of real-time mental stress monitoring system and personalized mental stress assessment modality.