Article,
Genetic programming for epileptic pattern recognition in electroencephalographic signals
Applied Soft Computing, 7 (1): 343--352 (January 2007)
DOI: doi:10.1016/j.asoc.2005.07.004
Abstract
the genetic programming paradigm, in conjunction with
pattern recognition principles, can be used to evolve
classifiers capable of recognising 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: 3 Hz 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.
