Name
Abstract | ||
|---|---|---|
Epilepsy is a widespread disorder that affects many individuals worldwide. For this reason much work has been done to develop computational systems that can facilitate the analysis and interpretation of the signals generated by a patients brain during the onset of an epileptic seizure. Currently, this is done by human experts since computational methods cannot achieve a similar level of performance. This paper presents a Genetic Programming (GP) based approach to analyze brain activity captured with Electrocorticogram (ECoG). The goal is to evolve classifiers that can detect the three main stages of an epileptic seizure. Experimental results show good performance by the GP-classifiers, evaluated based on sensitivity, specificity, prevalence and likelihood ratio. The results are unique within this domain, and could become a useful tool in the development of future treatment methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31519-0_4 | EVOLVE - A BRIDGE BETWEEN PROBABILITY, SET ORIENTED NUMERICS, AND EVOLUTIONARY COMPUTATION II |
Keywords | Field | DocType |
Epilepsy Diagnosis,Genetic Programming,Classification | Computer science,Genetic programming,Brain activity and meditation,Epilepsy,Epileptic seizure,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
175 | 2194-5357 | 1 |
PageRank | References | Authors |
0.36 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arturo Sotelo | 1 | 2 | 0.70 |
| Enrique Guijarro | 2 | 3 | 1.86 |
| Leonardo Trujillo | 3 | 444 | 38.12 |
| Luis N Coria | 4 | 3 | 1.48 |
| Yuliana Martínez | 5 | 42 | 5.70 |