WaaS architecture-driven depressive mood status quantitative analysis based on forehead EEG and self-rating tool

Abstract

Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log. The Leave-One-Participant-Out Cross-Validation is adopted to estimate the performance of the quantitative model, and the result shows that the model outcomes have a moderate uphill relationship (the average coefficient equals 0.6556 and the P value less than 0.01) with the FPC values of the Q-Log. Furthermore, an exemplary application of knowledge sharing, which is developed by using ontology technology and Jena inference subsystem, is given to illustrate the preliminary work for annotating data and facilitating clinical users to understand the meaning of the quantitative analysis results. This method combining physiological sensor data with psychological self-rating data could provide new insights into the pervasive and objective depression evaluation processes in daily life.