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Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers

Citation

Hurta, M and Drahosova, M and Sekanina, L and Smith, SL and Alty, JE, Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers, Genetic Programming (Lecture Notes in Computer Science, vol 13223), Springer, Cham, E Medvet, G Pappa, and B Xue (ed), Switzerland, pp. 85-101. ISBN 978-3-031-02055-1 (2022) [Research Book Chapter]

Copyright Statement

Copyright 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

DOI: doi:10.1007/978-3-031-02056-8_6

Abstract

Parkinsonís disease is one of the most common neurological conditions whose symptoms are usually treated with a drug containing levodopa. To minimise levodopa side effects, i.e. levodopa-induced dyskinesia (LID), it is necessary to correctly manage levodopa dosage. This article covers an application of cartesian genetic programming (CGP) to assess LID based on time series collected using accelerators attached to the patientís body. Evolutionary design of reduced precision classifiers of LID is investigated in order to find a hardware-efficient classifier together with classification accuracy as close as possible to a baseline software implementation. CGP equipped with the coevolution of adaptive size fitness predictors (coASFP) is used to design LID-classifiers working with fixed-point arithmetics with reduced precision, which is suitable for implementation in application-specific integrated circuits. In this particular task, we achieved a significant evolutionary design computational cost reduction in comparison with the original CGP. Moreover, coASFP effectively prevented overfitting in this task. Experiments with reduced precision LID-classifier design show that evolved classifiers working with 8-bit unsigned integer data representation, together with the input data scaling using the logical right shift, not only significantly outperformed hardware characteristics of all other investigated solutions but also achieved a better classifier accuracy in comparison with classifiers working with the floating-point numbers.

Item Details

Item Type:Research Book Chapter
Keywords:Parkinson's, movement analysis, artificial intelligence
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Neurology and neuromuscular diseases
Objective Division:Health
Objective Group:Clinical health
Objective Field:Diagnosis of human diseases and conditions
UTAS Author:Alty, JE (Associate Professor Jane Alty)
ID Code:153828
Year Published:2022
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2022-10-10
Last Modified:2022-12-16
Downloads:0

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