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

doi:10.1109/EMBC46164.2021.9630106

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 proceeding › Conference contribution

6 Citations (Scopus)

89 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 language	English
Title of host publication	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)
Publisher	IEEE
Pages	1702-1706
Number of pages	5
ISBN (Electronic)	9781728111797
DOIs	https://doi.org/10.1109/EMBC46164.2021.9630106
Publication status	Published - 9 Dec 2021
Event	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021 - Virtual, Virtual, Online, Mexico Duration: 1 Nov 2021 → 5 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 2021
Abbreviated title	EMBC 2021
Country/Territory	Mexico
City	Virtual, Online
Period	1/11/21 → 5/11/21
Internet address	https://embc.embs.org/2021/

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EMBC46164.2021.9630106

Download pdf
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 448 KB

Cite this

@inproceedings{8818ad973dc244bf90b02a88432bd9ad,

title = "Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data",

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.",

keywords = "Parkinson's disease, Recurrent neural networks, LSTM, Echo State Networks, Diagnosis",

author = "Shenoy, {A. V. Arjun} and Lones, {Michael A.} and Smith, {Stephen L.} and Marta Vallejo",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021, EMBC 2021 ; Conference date: 01-11-2021 Through 05-11-2021",

year = "2021",

month = dec,

day = "9",

doi = "10.1109/EMBC46164.2021.9630106",

language = "English",

pages = "1702--1706",

booktitle = "43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)",

publisher = "IEEE",

address = "United States",

url = "https://embc.embs.org/2021/",

}

Shenoy, AVA, Lones, MA, Smith, SL & Vallejo, M 2021, Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data. in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021). IEEE, pp. 1702-1706, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021, Virtual, Online, Mexico, 1/11/21. https://doi.org/10.1109/EMBC46164.2021.9630106

Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data. / Shenoy, A. V. Arjun; Lones, Michael A.; Smith, Stephen L. et al.
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021). IEEE, 2021. p. 1702-1706.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data

AU - Shenoy, A. V. Arjun

AU - Lones, Michael A.

AU - Smith, Stephen L.

AU - Vallejo, Marta

N1 - Conference code: 43

PY - 2021/12/9

Y1 - 2021/12/9

N2 - 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.

AB - 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.

KW - Parkinson's disease

KW - Recurrent neural networks

KW - LSTM

KW - Echo State Networks

KW - Diagnosis

UR - http://www.scopus.com/inward/record.url?scp=85114399336&partnerID=8YFLogxK

U2 - 10.1109/EMBC46164.2021.9630106

DO - 10.1109/EMBC46164.2021.9630106

M3 - Conference contribution

C2 - 34891614

AN - SCOPUS:85114399336

SP - 1702

EP - 1706

BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)

PB - IEEE

T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021

Y2 - 1 November 2021 through 5 November 2021

ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this