Automatic analysis of Categorical Verbal Fluency for Mild Cognitive Impartment detection: a non-linear language independent approach
| Autor: | Karmele López-de-Ipiña, Pablo Martinez-Lage, Mirian Ecay-Torres, Marcos Faundez-Zanuy, F. Torres, Nora Barroso, U. Martinez-de-Lizarduy |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) 02 engineering and technology computer.software_genre Quantitative Biology - Quantitative Methods behavioral disciplines and activities Computer Science - Sound 03 medical and health sciences 0302 clinical medicine Audio and Speech Processing (eess.AS) 0202 electrical engineering electronic engineering information engineering medicine FOS: Electrical engineering electronic engineering information engineering Dementia Verbal fluency test Permutation entropy Cognitive impairment Severe disability Categorical variable Quantitative Methods (q-bio.QM) business.industry Cognition medicine.disease Support vector machine FOS: Biological sciences 020201 artificial intelligence & image processing Artificial intelligence Psychology business computer 030217 neurology & neurosurgery Natural language processing Cognitive psychology Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | IWOBI |
| DOI: | 10.48550/arxiv.2203.09878 |
| Popis: | Alzheimer's disease (AD) is one the main causes of dementia in the world and the patients develop severe disability and sometime full dependence. In previous stages Mild Cognitive Impairment (MCI) produces cognitive loss but not severe enough to interfere with daily life. This work, on selection of biomarkers from speech for the detection of AD, is part of a wide-ranging cross study for the diagnosis of Alzheimer. Specifically in this work a task for detection of MCI has been used. The task analyzes Categorical Verbal Fluency. The automatic classification is carried out by SVM over classical linear features, Castiglioni fractal dimension and Permutation Entropy. Finally the most relevant features are selected by ANOVA test. The promising results are over 50% for MCI Comment: 4 pages, published in 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI), pp. 101-104 |
| Databáze: | OpenAIRE |
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