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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
Parkinson’s 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 patient’s 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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