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

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

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 proceeding › Conference contribution

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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 language	English
Title of host publication	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher	IEEE
Number of pages	5
Publication status	Published - Nov 2021
Event	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Virtual Duration: 31 Oct 2021 → 4 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 title	EMBC
Period	31/10/21 → 4/11/21
Internet address	https://embc.embs.org/2021/

Keywords

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

UN SDGs

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

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Cite this

@inproceedings{8818ad973dc244bf90b02a88432bd9ad,

title = "Evaluation of recurrent neural network models for Parkinson{\textquoteright}s disease classification using drawing data",

abstract = "Parkinson{\textquoteright}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{\textquoteright}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 = "Arjun Shenoy and Lones, {Michael Adam} and Smith, {Stephen L.} and Marta Vallejo",

year = "2021",

month = nov,

language = "English",

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

publisher = "IEEE",

address = "United States",

note = " 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC ; Conference date: 31-10-2021 Through 04-11-2021",

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

}

Evaluation of recurrent neural network models for Parkinson’s disease classification using drawing data. / Shenoy, Arjun ; Lones, Michael Adam; Smith, Stephen L.; Vallejo, Marta.

43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2021.

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, Arjun

AU - Lones, Michael Adam

AU - Smith, Stephen L.

AU - Vallejo, Marta

N1 - Conference code: 43

PY - 2021/11

Y1 - 2021/11

N2 - 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.

AB - 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.

KW - Parkinson's disease

KW - Recurrent neural networks

KW - LSTM

KW - Echo State Networks

KW - Diagnosis

M3 - Conference contribution

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

PB - IEEE

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

Y2 - 31 October 2021 through 4 November 2021

ER -

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

Abstract

Conference

Keywords

UN SDGs

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