A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

A. T. Tzallas; P. S. Karvelis; C. D. Katsis; D. I. Fotiadis; S. Giannopoulos; S. Konitsiotis

doi:10.1055/s-0038-1634122

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2006; 45(06): 610-621
DOI: 10.1055/s-0038-1634122

Original Article

Schattauer GmbH

A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

A. T. Tzallas

¹Dept. of Medical Physics, Medical School, University of Ioannina, Ioannina, Greece

²Unit of Medical Technology and Intelligent Information Systems, Dept. of Computer Science, University of Ioannina, Ioannina, Greece

,

P. S. Karvelis

²Unit of Medical Technology and Intelligent Information Systems, Dept. of Computer Science, University of Ioannina, Ioannina, Greece

,

C. D. Katsis

¹Dept. of Medical Physics, Medical School, University of Ioannina, Ioannina, Greece

²Unit of Medical Technology and Intelligent Information Systems, Dept. of Computer Science, University of Ioannina, Ioannina, Greece

,

D. I. Fotiadis

²Unit of Medical Technology and Intelligent Information Systems, Dept. of Computer Science, University of Ioannina, Ioannina, Greece

³Biomedical Research Institute – FORTH, Ioannina, Greece

,

S. Giannopoulos

⁴Dept. of Neurology, Medical School, University of Ioannina, Ioannina, Greece

,

S. Konitsiotis

⁴Dept. of Neurology, Medical School, University of Ioannina, Ioannina, Greece

› Author Affiliations

Abstract

Summary

Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method.

Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity.

Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases.

Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Keywords

EEG - automated epilepsy diagnosis - clustering - artificial neural networks - spike detection - knowledge-based system

Full Text

References

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