Abstract
BACKGROUND: Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predominantly depend on subjective and self-reported methods. There is an urgent necessity to develop objective means of identifying depression. Acoustic features, which convey emotional information, have the potential to enhance the objectivity of depression assessments. This study aimed to investigate the feasibility of utilizing acoustic features for the objective and automated identification and characterization of depression among Chinese university students. METHODS: A cross-sectional study was undertaken involving 103 students with depression and 103 controls matched for age, gender, and education. Participants' voices were recorded using a smartphone as they read neutral texts. Acoustic analysis and feature extraction were performed using the OpenSMILE toolkit, yielding 523 features encompassing spectral, glottal, and prosodic characteristics. These extracted acoustic features were utilized for discriminant analysis between depression and control groups. Pearson correlation analyses were conducted to evaluate the relationship between acoustic features and Patient Health Questionnaire-9 (PHQ-9) scores. Five machine learning algorithms including Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Classification, Naive Bayes, and Random Forest were used to perform the classification. For training and testing, ten-fold cross-validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curve, area under the curve (AUC), precision, accuracy, recall, and F1 score. Shapley Additive exPlanations (SHAP) method was used for model interpretation. RESULTS: In depression group, 32 acoustic features (25 spectral features, 5 prosodic features and 2 glottal features) showed significant alterations compared with controls. Further, 27 acoustic features (10 spectral features, 3 prosodic features, and 1 glottal features) were significantly correlated with depression severity. Among five machine learning algorithms, LDA model demonstrated the highest classification performance, with an AUC of 0.771. SHAP analysis suggested that Mel-frequency cepstral coefficients (MFCC) features contributed most to the model's classification efficacy. CONCLUSIONS: The integration of acoustic features and LDA model demonstrates a high accuracy in distinguishing depression among Chinese university students, suggesting its potential utility in rapid and large-scale depression screening. MFCC may serve as objective and valid features for the automated identification of depression on Chinese university campuses.