Daphne B. G. ter Huurne1, Inez H.G.B. Ramakers1,2, Nicklas Linz3, Alexandra König4, Kai Langel5, Hali Lindsay3, Frans R.J. Verhey1,2 and Marjolein de Vugt1,2
1Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands, 2Maastricht University Medical Center, Maastricht, Netherlands, 3ki elements UG, Saarbrücken, Germany, 4National Institute for Research in Computer Science and Automation (INRIA), Sophia Antipolis, France, 5Janssen Clinical Innovation, Beerse, Belgium
Talk presented at AAIC 2021.
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.