Identifying Parkinson’s Disease Through the Classification of Audio Recording Data

Date

2020-07

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

Developments in artificial intelligence can be leveraged to support the diagnosis of degenerative disorders, such as epilepsy and Parkinson’s disease. This study aims to provide a software solution, focused initially towards Parkinson’s disease, which can positively impact medical practice surrounding degenerative diagnoses. Through the use of a dataset containing numerical data representing acoustic features extracted from an audio recording of an individual, it is determined if a neural approach can provide an improvement over previous results in the area. This is achieved through the implementation of a feedforward neural network and a layer recurrent neural network. By comparison with the state-of-the-art, a Bayesian approach providing a classification accuracy benchmark of 87.1%, it is found that the implemented neural networks are capable of average accuracy of 96%, highlighting improved accuracy for the classification process. The solution is capable of supporting the diagnosis of Parkinson’s disease in an advisory capacity and is envisioned to inform the process of referral through general practice.

Description

Keywords

Parkinson’s disease, recurrent neural network, audio processing, pre-diagnostic tools

Citation

Bielby, J., Kuhn, S., Colreavy-Donnelly, S., Caraffini, F., O'Connor, S., Anastassi, Z. (2020) Identifying Parkinson’s Disease Through the Classification of Audio Recording Data. IEEE World Congress on Computational Intelligence (IEEE WCCI), Glasgow, UK, July 2020.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)