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Clinical use of deep speech parameters derived from the semantic verbal fluency task
Author(s) -
ter Huurne Daphne BG,
Ramakers Inez HGB,
Linz Nicklas,
König Alexandra,
Langel Kai,
Lindsay Hali,
Verhey Frans RJ,
de Vugt Marjolein
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.050380
Subject(s) - verbal fluency test , fluency , dementia , raw score , clinical dementia rating , psychology , cognition , rating scale , audiology , disease , medicine , neuropsychology , developmental psychology , computer science , raw data , psychiatry , mathematics education , programming language
Background Previous research showed that semantic memory is a good indicator for cognitive decline in early phases of Alzheimer’s disease. Automatically derived deep speech parameters from the semantic verbal fluency (SVF) can potentially have an additional value to differentiate SCI, MCI and dementia, compared to the total fluency score. However, the added diagnostic value of (specific) deep speech parameters to the commonly clinically used SVF raw score remains unknown. In the Deepspa project, we investigated the (additional) value of automatically derived speech parameters in clinical practice. We also investigated the relation between automatically derived speech parameters of the SVF and other cognitive tasks, as well as disease severity and functioning in daily living. Method In the DeepSPA project, 140 participants were recruited from the memory clinic of the MUMC+ (SCI, MCI, ADD). All subjects underwent a cognitive assessment including the SVF. The SVF (animals, 60 seconds) was administered by use of the Delta application (ki elements). Disease severity and functioning in daily life were administered by the Clinical Dementia Rating Scale (CDR) and Disability Assessment for Dementia (DAD) respectively. The agreement between the automatic and clinical raw score of the SVF was assessed by the interclass correlation coefficient. The relation between the deep speech parameters, such as mean word frequency, temporal and semantic clusters, transition time between words etc., and syndrome diagnosis, DAD and CDR were investigated using stepwise regression analyses, corrected for age, education level and gender. Result Preliminary results showed that deep speech parameters have an additional value in the classification of clinical diagnosis, and disease severity. More specifically, mean word frequency and the mean time between temporal clusters had an added value. The reliability between word count by the application and the clinician was good (ICC=.882, 95% CI = .820‐.923). Results about the relation between deep speech parameters and other cognitive performances will be available at the conference. Conclusion First results suggest that deep speech parameters have an additional value in the early diagnostics of cognitive impairments. More information about the value of these non‐invasive automatically derived deep speech parameters could improve diagnostic accuracy in clinical practice.