Publication: Classification of Depression and Other Psychiatric Conditions Using Speech Features Extracted from a Thai Psychiatric and Verbal Screening Test
Issued Date
2021-01-01
Resource Type
ISSN
1557170X
Other identifier(s)
2-s2.0-85122532129
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Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. (2021), 651-656
Suggested Citation
N. Klangpornkun, M. Ruangritchai, A. Munthuli, C. Onsuwan, K. Jaisin, K. Pattanaseri, J. Lortrakul, P. Thanakulakkarachai, T. Anansiripinyo, A. Amornlaksananon, S. Laohawee, C. Tantibundhit Classification of Depression and Other Psychiatric Conditions Using Speech Features Extracted from a Thai Psychiatric and Verbal Screening Test. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. (2021), 651-656. doi:10.1109/EMBC46164.2021.9629571 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76720
Research Projects
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Title
Classification of Depression and Other Psychiatric Conditions Using Speech Features Extracted from a Thai Psychiatric and Verbal Screening Test
Other Contributor(s)
Abstract
Depression is a common and serious mental illness which negatively affects daily functioning. To prevent the progression of the illness into severe or long-term consequences, early diagnosis is crucial. We developed an automated speech feature analysis application for depression and other psychiatric disorders derived from a developed Thai psychiatric and verbal screening test. The screening test includes Thai's version of Patient Health Questionnaire-9 (PHQ-9) and Hamilton Depression Rating Scale (HAM-D), and 32 additional emotion-induced questions. Case-control study was conducted on speech features from 66 participants. Twenty seven of those had depression (DP), 12 had other psychiatric disorders (OP), and 27 were normal controls (NC). The five-fold cross-validation from 6 settings of 5 classifiers with the combination of PHQ-9 and HAM-D scores, and speech features were examined. Results showed highest performance from the multilayer perceptron (MLP) classifier which yielded 83.33% sensitivity, 91.67% specificity, and 83.33% accuracy, where negative-emotional questions were most effective in classification. The automated speech feature analysis showed promising results for screening patients with depression or other psychiatric disorders. The current application is accessible through smartphone, making it a feasible and intuitive setup for low-resource countries such as Thailand.