An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs

Authors:
Steven Walczak;William J. Nowack
Affiliations:
University of Colorado at Denver, College of Business and Administration, Campus Box 165, P.O. Box 173364, Denver, Colorado 80217-3364. (303) 556-6777. (303) 556-5899 fax/ swalczak@carbon.cudenver ...;Department of Neurology, University of South Alabama, 2451 Fillingim Street, MCSB–/Suite 1102, Mobile, Alabama 36617-2293/ wnowack@usamail.usouthal.edu
Venue:
Journal of Medical Systems
Year:
2001

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Abstract

Determining the cause of seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.