title: Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.
abstract: BACKGROUND
  In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training.
  
  
  METHODS
  The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats.
  
  
  METHODS
  During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques.
  
  
  RESULTS
  A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%.
  
  
  CONCLUSIONS
  Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.
authors.affiliation: Electronics and Information Systems, Ghent University, Belgium. pieter.buteneers@ugent.be
authors.forename: Pieter
authors.initials: P
authors.lastname: Buteneers
authors.forename: David
authors.initials: D
authors.lastname: Verstraeten
authors.forename: Pieter
authors.initials: P
authors.lastname: van Mierlo
authors.forename: Tine
authors.initials: T
authors.lastname: Wyckhuys
authors.forename: Dirk
authors.initials: D
authors.lastname: Stroobandt
authors.forename: Robrecht
authors.initials: R
authors.lastname: Raedt
authors.forename: Hans
authors.initials: H
authors.lastname: Hallez
authors.forename: Benjamin
authors.initials: B
authors.lastname: Schrauwen
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D001921:Brain / Q000503:physiopathology*; D058256:Brain Waves; D004195:Disease Models, Animal; D004569:Electroencephalography; D004832:Epilepsy, Absence / Q000175:diagnosis* / Q000235:genetics / Q000503:physiopathology; D004830:Epilepsy, Tonic-Clonic / Q000139:chemically induced / Q000175:diagnosis* / Q000503:physiopathology; D007608:Kainic Acid; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D051381:Rats; D017208:Rats, Wistar; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
chemical_list: D007608:Kainic Acid