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.
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Acknowledgements
This work was supported by the Czech science foundation project 21-13001S and by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140).
The author would like to thank Dr. Vojtech Mrazek for his help with circuit synthesis. We also acknowledge the patients and clinical staff of Leeds Teaching Hospitals NHS Trust, particularly Dr Stuart Jamison and Dr Jeremy Cosgrove, for their contribution to the clinical study that generated the data used in this research, Dr Michael Lones for his help and advice with regards to the technical aspects, and also the UK National Institute for Health Research (NIHR) for adopting the study within in its Clinical Research Network Portfolio.
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Hurta, M., Drahosova, M., Sekanina, L., Smith, S.L., Alty, J.E. (2022). Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_6
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