A depressive mood status quantitative reasoning method based on portable EEG and self-rating scale

In order to actualize the quantitative reasoning function into the portable brain and mental health-monitoring system under WaaS architecture, this study proposes a method named quantitative reasoning for depressive mood status based on portable EEG and self-rating scale data. 5 inpatients were recruited to join the experiment, from which the portable EEG data and clinical self-rating scale data of 2 weeks were collected. The principal component analysis method is adopted to process the self-rating data. The regression analysis based on random forest algorithm is used to generate the quantitative reasoning model for acquiring reasoning rules. In order to further implement the quantitative reasoning function, the Protege and Jena are adopted to build data ontologies and actualize an automatic reasoning function for objectively quantifying the depressive mood status respectively. The effectiveness of reasoning rules are validated, the preliminary results show that the expected quantitative value outputted from the quantitative reasoning model is highly correlated (the absolute value of correlation coefficient ≥0.7, P-value ≤0.05) with the actual self-rating scale data.