Parkinson's disease diagnostics using AI and natural language knowledge transfer

• URL: http://arxiv.org/abs/2204.12559v1
• Date: Tue, 26 Apr 2022 19:39:29 GMT
• Title: Parkinson's disease diagnostics using AI and natural language knowledge transfer
• Authors: Maurycy Chronowski, Maciej Klaczynski, Malgorzata Dec-Cwiek, Karolina Porebska
• Abstract summary: Deep learning approach for classification of raw speech recordings in patients with diagnosed PD was proposed. Method was tested on a group of 38 PD patients and 10 healthy persons above the age of 50.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, the issue of Parkinson's disease (PD) diagnostics using non-invasive antemortem techniques was tackled. A deep learning approach for classification of raw speech recordings in patients with diagnosed PD was proposed. The core of proposed method is an audio classifier using knowledge transfer from a pretrained natural language model, namely \textit{wav2vec 2.0}. Method was tested on a group of 38 PD patients and 10 healthy persons above the age of 50. A dataset of speech recordings acquired using a smartphone recorder was constructed and the recordings were label as PD/non-PD with severity of the disease additionally rated using Hoehn-Yahr scale. The audio recordings were cut into 2141 samples that include sentences, syllables, vowels and sustained phonation. The classifier scores up to 97.92\% of cross-validated accuracy. Additionally, paper presents results of a human-level performance assessment questionnaire, which was consulted with the neurology professionals

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