التفاصيل البيبلوغرافية
| العنوان: |
Computer-based detection of depression and dementia in spontaneous speech. |
| المؤلفون: |
Chlasta, K., Holas, P., Wolk, K. |
| المصدر: |
European Psychiatry; 2022 Supplement S1, Vol. 64, pS349-S349, 1/3p |
| مصطلحات موضوعية: |
SPEECH, ARTIFICIAL neural networks, DEMENTIA, MENTAL depression, MENTAL illness, ANXIETY disorders |
| مستخلص: |
Introduction: There is a significant relation between old-age depression and subsequent dementia in patients aged 50. This supports the hypothesis of old-age depression being a predictor, and possibly a causal factor, of subsequent dementia. The number of people aged 60 years and over has tripled since 1950, reaching 16% in 2050, leading to new medical challenges. Depression is the most common mental disorder in older adults, affecting 7%of the older population. Dementia is the second most common with about 5% prevalence worldwide, but it is the first leading cause of disease burden. Objectives: Early detection and treatment is essential in promoting remission, preventing relapse, and reducing emotional burden. Speech is a well established early indicator of cognitive deficits. Speech processing methods offer great potential to fully automatically screen for prototypic indicators of both dementia and depressive disorders. Methods: We present two different methods to detect pathological speech with artificial neural networks. We use both deep architectures, as well as more traditional machine learning approaches. Results: The models developed using a two-stage deep architecture achieved 59% classification accuracy on the test set from DementiaBank. Our CNN system achieved the best classification accuracy of 63.6% for dementia, but reaching 70% for depressive disorders on the test set from Distress Analysis Interview Corpus. Conclusions: These methods offer a promising classification accuracy ranging from 63% to 70%, applicable in an innovative speechbased screening system. [ABSTRACT FROM AUTHOR] |
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