Open AccessNEURAL-NETWORK SEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS FOR EPILEPTIFORM ACTIVITY DETECTION
Author(s) -
Svetlana V. Bezobrazova,
Vladimir Golovko
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.7.3.521
Subject(s) - electroencephalography , segmentation , pattern recognition (psychology) , computer science , artificial intelligence , artificial neural network , anomaly detection , signal (programming language) , neuroscience , psychology , programming language
A goal of EEG signals analysis is not only human psychologically and functionality states definition but also pathological activity detection. In this paper we present an approach for epileptiform activity detection by artificial neural network technique for EEG signal segmentation and for the highest Lyapunov’s exponent computing. The EEG segmentation by the neural network approach makes it possible to detect an abnormal activity in signals. We examine our system for segmentation and anomaly detection on the EEG signals where the anomaly is an epileptiform activity.