Selected Publications

Background: The neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient’s quality of life. Automatic seizure detection can help improve a patient’s interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. Methods: In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. Results: Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. Comparison with existing methods: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. Conclusions: The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.
Journal of Neuroscience Methods, 2016.

Recent Publications

More Publications

Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation

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Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming

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Automatic Modeling of a Gas Turbine using Genetic Programming: An Experimental Study

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Local Search is Underused in Genetic Programming

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Local Search Approach to Genetic Programming for RF-PAs Modeling Implemented in FPGA

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Recent & Upcoming Talks

NEO 2017

Sep 27, 2017, NEO Workshop 2017

Teaching

I am professor for the following courses at Instituto Tecnologico de Tijuana

  • ACF-0901IN1C Calculus: Diferential calculus (under-graduated)
  • ACF-0901IN1M Calculus: Diferential calculus (under-graduated)
  • INC-1027IN7F Simulation: Modeling and statistical simulation (under-graduated)
  • SSD-1505BM9A Biosignals analysis: Signal analysis, processing and recognition (under-graduated)
  • AEF-1024IN3A Statistical inference I: Basic statistical inference (under-graduated)
  • MIIN30202M: Statistics: Advanced statistical inference (post-graduated)

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