Evaluation of recurrent neural network models for Parkinson’s disease classification using drawing data

Arjun Shenoy, Michael Adam Lones, Stephen L. Smith, Marta Vallejo

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

Abstract

Parkinson’s disease is a disorder that affects the neurons in the human brain. It is characterized by various symptoms such as slowness of motor functions (bradykinesia), motor instability, speech impairment and, in some cases, psychiatric effects such as hallucinations. Most of the symptoms mentioned here, however, are also common side effects of normal 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 publication 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Publication statusAccepted/In press - 15 Jul 2021
Event 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Virtual
Duration: 31 Oct 20214 Nov 2021
Conference number: 43
https://embc.embs.org/2021/

Conference

Conference 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC
Period31/10/214/11/21
Internet address

Keywords

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

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