A neural-network-based detection of epilepsy

• Published in: Neurological research (New York) Vol. 26; no. 1; p. 55
• Main Authors: Nigam, Vivek Prakash, Graupe, Daniel
• Format: Journal Article
• Language: English
• Published: England 01.01.2004
• Subjects: Algorithms; Artificial Intelligence; Diagnostic Errors - prevention & control; Electroencephalography - instrumentation; Electroencephalography - methods; Electroencephalography - trends; Epilepsy - diagnosis; Epilepsy - physiopathology; Humans; Neural Networks (Computer); Predictive Value of Tests; Reproducibility of Results; Signal Processing, Computer-Assisted - instrumentation
• ISSN: 0161-6412
• DOI: 10.1179/016164104773026534

Diagnosis of epilepsy is primarily based on scalp-recorded electroencephalograms (EEG). Unfortunately the long-term recordings obtained from 'ambulatory recording systems' contain EEG data of up to one week duration, which has introduced new problems for clinical analysis. Traditional methods, where the entire EEG is reviewed by a trained professional, are very time-consuming when applied to recordings of this length. Therefore, several automated diagnostic aid approaches were proposed in recent years, in order to reduce expert effort in analyzing lengthy recordings. The most promising approaches to automated diagnosis are based on neural networks. This paper describes a method for automated detection of epileptic seizures from EEG signals using a multistage nonlinear pre-processing filter in combination with a diagnostic (LAMSTAR) Artificial Neural Network (ANN). Pre-processing via multistage nonlinear filtering, LAMSTAR input preparation, ANN training and system performance (1.6% miss rate, 97.2% overall accuracy when considering both false-alarms and 'misses') are discussed and are shown to compare favorably with earlier approaches presented in recent literature.