Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students

• Main Authors: Muhamad Fikri, Zanil, Nik Hashim, Nik Nur Wahidah, Azam, Huda
• Format: Conference or Workshop Item
• Language: English; English
• Published: IOP Publishing 2017
• Subjects: T Technology (General)
• Online Access: http://irep.iium.edu.my/61222/7/61222-PREDICTION%20OF%20BECK%20DEPRESSION%20INVENTORY.pdf; http://irep.iium.edu.my/61222/8/61222-Prediction%20of%20Beck%20Depression%20Inventory-SCOPUS.pdf; http://irep.iium.edu.my/61222/; http://iopscience.iop.org/article/10.1088/1757-899X/260/1/012022/pdf

Psychiatrist currently relies on questionnaires and interviews for psychological assessment. These conservative methods often miss true positives and might lead to death, especially in cases where a patient might be experiencing suicidal predisposition but was only diagnosed as major depressive disorder (MDD). With modern technology, an assessment tool might aid psychiatrist with a more accurate diagnosis and thus hope to reduce casualty. This project will explore on the relationship between speech features of spoken audio signal (reading) in Bahasa Malaysia with the Beck Depression Inventory scores. The speech features used in this project were Power Spectral Density (PSD), Mel-frequency Ceptral Coefficients (MFCC), Transition Parameter, formant and pitch. According to analysis, the optimum combination of speech features to predict BDI-II scores include PSD, MFCC and Transition Parameters. The linear regression approach with sequential forward/backward method was used to predict the BDI-II scores using reading speech. The result showed 0.4096 mean absolute error (MAE) for female reading speech. For male, the BDI-II scores successfully predicted 100% less than 1 scores difference with MAE of 0.098437. A prediction system called Depression Severity Evaluator (DSE) was developed. The DSE managed to predict one out of five subjects. Although the prediction rate was low, the system precisely predict the score within the maximum difference of 4.93 for each person. This demonstrates that the scores are not random numbers.