Automatic subtyping of individuals with Primary Progressive Aphasia

Objective The classification of patients with Primary Progressive Aphasia into variants is time consuming, costly, and requires combined evaluations by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. Therefore, our aim is to determine if acoustic and linguistic variables provide accurate classification of PPA patients into one of the three variants.

Methods In this paper, we present a machine learning model based on Deep Neural Networks for the subtyping of patients with PPA into the three main variants using combined acoustic and linguistic information elicited automatically using acoustic and linguistic analysis. The performance of the Deep Neural Networks was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees. It was also compared to the classification based on auditory scores provided by clinicians.

Results The DNN model resulted in 80% classification accuracy providing reliable subtyping of patients with PPA into variants that outperformed other machine learning models and auditory classification of patients into variants by clinicians.

Conclusion We show that combined measures of speech and language function as the patients’ fingerprint and provide information about patients’ symptoms and variant subtyping. This approach can enable clinicians and researchers to employ this fingerprint and provide an automatic classification of patients with PPA saving much time and money.