A Quantitative Analysis Method for Objectively Assessing the Depression Mood Status Based on Portable EEG and Self-rating Scale

Abstract

In order to recognize the major depressive mood status of inpatients and achieve its daily change information, a POMS-BCN scale was used to rate the mood status. Meanwhile, a personalized quantified model based on portable EEG was built, which aimed at objectively assessing the major depressive mood status for each patient. 6 inpatients were recruited to join the experiment. The Principal Component Analysis method is used to extract first principal component curve from the POMS-BCN data. The feature extraction method is used to extract linear and nonlinear features from portable EEG data. The regression analysis based on Random Forest is adopted to build the personalized quantified model. The principal component analysis result shows that the first principal component curve is able to recognize the major emotional factor and depict its daily change information. Additionally, the expected quantitative value outputted from the personalized quantified model is highly correlated (the absolute value of correlation coefficient 0.7, P-value 0.05) with the actual first principal component data, which implies that the personalized quantified model can give an accurate objective assessment for the major depressive mood status.