EWA - Early Warning of Alzheimer

Včasné zistenie príznakov Alzheimerovej choroby a iných neurodegeneratívnych chorôb

Detecting Alzheimer’s disease

Analysis of spontaneous speech data elicited through Cookie Theft picture description (1-5 minute samples) from 240 people probable AD category and 233 healthy controls. Linguistic and acoustic variables used to train a machine learning classifier to distinguish between the groups, with 82% accuracy.

Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease, 49(2), 407-422.

Predicting MMSE for AD monitoring

A temporal Bayes network trained on 182 lexicosyntactic, 210 acoustic, and 85 semantic features extracted from 393 spontaneous speech samples elicited through Cookie Theft picture description can predict MMSE scores with a mean absolute error of 3.8, comparable to within-subject interrater (clinician) standard deviation of 3.9 to 4.8.

Yancheva, M., Fraser, K., & Rudzicz, F. (2015). Using Linguistic Features Longitudinally to Predict Clinical Scores for Alzheimer’s Disease and Related Dementias.

Subtyping primary progressive aphasia

Syntactic and semantic features were automatically extracted from transcriptions of narrative speech for three groups: semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. Machine learning classifiers trained on these features were able to distinguish between the three participant groups with up to 100% accuracy.

Fraser, K.C., Meltzer, J.A., Graham, N.L., Leonard, C., Hirst, G., Black, S.E., & Rochon, E. (2014). Automated classification of primary progressive aphasia subtypes from narrative speech transcripts. Cortex, 55, 43-60.

Winterlight Labs