Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Real-time computer aided analysis of the electroencephalogram: a two-dimensional approach
Real-time computer aided analysis of the electroencephalogram: a two-dimensional approach
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Waveform recognition using genetic programming: the myoelectric signal recognition problem
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
Hyperspectral image analysis using genetic programming
Applied Soft Computing
Artificial Intelligence in Medicine
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
Using feature-based fitness evaluation in symbolic regression with added noise
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Dynamic population variation in genetic programming
Information Sciences: an International Journal
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
Identification of epilepsy stages from ECoG using genetic programming classifiers
Computers in Biology and Medicine
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This paper reports how the genetic programming paradigm, in conjunction with pattern recognition principles, can be used to evolve classifiers capable of recognizing epileptic patterns in human electroencephalographic signals. The procedure for feature extraction from the raw signal is detailed, as well as the genetic programming system that properly selects the features and evolves the classifiers. Based on the data sets used, two different epileptic patterns were detected: 3Hz spike-and-slow-wave-complex (SASWC) and spike-or-sharp-wave (SOSW). After training, classifiers for both patterns were tested with unseen instances, and achieved sensibility=1.00 and specificity=0.93 for SASWC patterns, and sensibility=0.94 and specificity=0.89 for SOSW patterns. Results are very promising and suggest that the methodology presented can be applied to other pattern recognition tasks in complex signals.