Publication detail

Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers

HURTA, M. DRAHOŠOVÁ, M. SEKANINA, L. SMITH, S. ALTY, J.

Original Title

Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers

Type

conference paper

Language

English

Original 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.

Keywords

Cartesian genetic programming, Coevolution, Adaptive size fitness predictors, Energy-efficient, Hardware-oriented, Fixed-point arithmetic, Levodopa-induced dyskinesia, Parkinsons disease

Authors

HURTA, M.; DRAHOŠOVÁ, M.; SEKANINA, L.; SMITH, S.; ALTY, J.

Released

22. 4. 2022

Publisher

Springer Nature Switzerland AG

Location

Madrid

ISBN

978-3-031-02055-1

Book

Genetic Programming, 25th European Conference, EuroGP 2022

Edition

Lecture Notes in Computer Science

Pages from

85

Pages to

101

Pages count

17

URL

https://link.springer.com/chapter/10.1007/978-3-031-02056-8_6

BibTex

@inproceedings{BUT177631,
  author="HURTA, M. and DRAHOŠOVÁ, M. and SEKANINA, L. and SMITH, S. and ALTY, J.",
  title="Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers",
  booktitle="Genetic Programming, 25th European Conference, EuroGP 2022",
  year="2022",
  series="Lecture Notes in Computer Science",
  volume="13223",
  pages="85--101",
  publisher="Springer Nature Switzerland AG",
  address="Madrid",
  doi="10.1007/978-3-031-02056-8\{_}6",
  isbn="978-3-031-02055-1",
  url="https://link.springer.com/chapter/10.1007/978-3-031-02056-8_6"
}