Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data

A. V. Arjun Shenoy, Michael A. Lones, Stephen L. Smith, Marta Vallejo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
18 Downloads (Pure)

Abstract

Parkinson's disease is a disorder that affects the neurons in the human brain. The various symptoms include slowness of motor functions (bradykinesia), motor instability, speech impairment and in some cases, psychiatric effects such as hallucinations. Most of these, however, are also common side effects of natural aging. This makes an accurate diagnosis of Parkinson's disease a challenging task. Some breakthroughs have been made in recent years with the help of deep learning. This work aims at considering figure drawing data as a time series of coordinates, angles and pressure readings to train recurrent neural network models. In addition, the work compares two recurrent network models, Long Short-Term Memory and Echo State Networks, to explore the advantages and disadvantages of both architectures.

Original languageEnglish
Title of host publication43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)
PublisherIEEE
Pages1702-1706
Number of pages5
ISBN (Electronic)9781728111797
DOIs
Publication statusPublished - 9 Dec 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021 - Virtual, Virtual, Online, Mexico
Duration: 1 Nov 20215 Nov 2021
Conference number: 43
https://embc.embs.org/2021/

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021
Abbreviated titleEMBC 2021
Country/TerritoryMexico
CityVirtual, Online
Period1/11/215/11/21
Internet address

Keywords

  • Parkinson's disease
  • Recurrent neural networks
  • LSTM
  • Echo State Networks
  • Diagnosis

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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