Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students
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 diso...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English |
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IOP Publishing
2017
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| Online Access: | http://irep.iium.edu.my/61222/ 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 |
| _version_ | 1848785633322991616 |
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| author | Muhamad Fikri, Zanil Nik Hashim, Nik Nur Wahidah Azam, Huda |
| author_facet | Muhamad Fikri, Zanil Nik Hashim, Nik Nur Wahidah Azam, Huda |
| author_sort | Muhamad Fikri, Zanil |
| building | IIUM Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T16:56:14Z |
| format | Proceeding Paper |
| id | iium-61222 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:56:14Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-612222018-06-26T08:11:21Z http://irep.iium.edu.my/61222/ Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students Muhamad Fikri, Zanil Nik Hashim, Nik Nur Wahidah Azam, Huda T Technology (General) 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. IOP Publishing 2017-11-07 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/61222/7/61222-PREDICTION%20OF%20BECK%20DEPRESSION%20INVENTORY.pdf application/pdf en http://irep.iium.edu.my/61222/8/61222-Prediction%20of%20Beck%20Depression%20Inventory-SCOPUS.pdf Muhamad Fikri, Zanil and Nik Hashim, Nik Nur Wahidah and Azam, Huda (2017) Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students. In: 6th International Conference on Mechatronics (ICOM'17), 8th-9th August 2017, Kuala Lumpur, Malaysia. http://iopscience.iop.org/article/10.1088/1757-899X/260/1/012022/pdf 10.1088/1757-899X/260/1/012022 |
| spellingShingle | T Technology (General) Muhamad Fikri, Zanil Nik Hashim, Nik Nur Wahidah Azam, Huda Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students |
| title | Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students |
| title_full | Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students |
| title_fullStr | Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students |
| title_full_unstemmed | Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students |
| title_short | Prediction of Beck Depression Inventory (BDI-II) score using acoustic measurements in a sample of Iium engineering students |
| title_sort | prediction of beck depression inventory (bdi-ii) score using acoustic measurements in a sample of iium engineering students |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/61222/ http://irep.iium.edu.my/61222/ http://irep.iium.edu.my/61222/ 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 |