- author = "Icaro {Marcelino Miranda} and Claus Aranha and Marcelo Ladeira",
- title = "Classification of {EEG} Signals using Genetic Programming for Feature Construction",
- booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion",
- year = "2019",
- editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
- pages = "1275--1283",
- address = "Prague, Czech Republic",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, Classification, EEG, Dimensionality Reduction, Feature Construction, Feature Selection, K Complex, Sleep Spindles",
- isbn13 = "978-1-4503-6111-8",
-
URL = "
http://human-competitive.org/sites/default/files/gecco_paper_2019.pdf",
-
DOI = "
doi:10.1145/3321707.3321737",
- size = "9 pages",
- abstract = "The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumours. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep spindles and K-complexes. The identification of these structures is usually performed by visual inspection from human experts, a process that can be error prone and susceptible to biases. Therefore there is interest in developing technologies for the automated analysis of EEG. In this paper, we propose a new Genetic Programming (GP) framework for feature construction and dimensionality reduction from EEG signals. We use these features to automatically identify spindles and K-complexes on data from the DREAMS project. Using 5 different classifiers, the set of attributes produced by GP obtained better AUC scores than those obtained from PCA or the full set of attributes. Also, the results obtained from the proposed framework obtained a better balance of Specificity and Recall than other models recently proposed in the literature. Analysis of the features most used by GP also suggested improvements for data acquisition protocols in future EEG examinations.",
-
notes = "2019 Humies finalist. Slides:
http://www.human-competitive.org/sites/default/files/marcelinoslides.pdf
Also known as \cite{3321737} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA-2019) and the 24th Annual Genetic Programming Conference (GP-2019)",
