title: A unified approach for detection of induced epileptic seizures in rats using ECoG signals.
abstract: OBJECTIVE
  Epileptic seizure detection is a key step for epilepsy assessment. In this work, using the pentylenetetrazole (PTZ) model, seizures were induced in rats, and ECoG signals in interictal, preictal, ictal, and postictal periods were recorded. The recorded ECoG signals were then analyzed to detect epileptic seizures in the epileptic rats.
  
  
  METHODS
  Two different approaches were considered in this work: thresholding and classification. In the thresholding approach, a feature is calculated in consecutive windows, and the resulted index is tracked over time and compared with a threshold. The moment the index crosses the threshold is considered as the moment of seizure onset. In the classification approach, features are extracted from before, during, and after ictal periods and statistically analyzed. Statistical characteristics of some features have a significant difference among these periods, thus resulting in epileptic seizure detection.
  
  
  RESULTS
  Several features were examined in the thresholding approach. Nonlinear energy and coastline features were successful in epileptic seizure detection. The best result was achieved by the coastline feature, which led to a mean of a 2-second delay in its correct detections. In the classification approach, the best result was achieved using the fuzzy similarity index that led to Pvalue<0.001.
  
  
  CONCLUSIONS
  This study showed that variance-based features were more appropriate for tracking abrupt changes in ECoG signals. Therefore, these features perform better in seizure onset estimation, whereas nonlinear features or indices, which are based on dynamical systems, can better track the transition of neural system to ictal period.
  
  
  CONCLUSIONS
  This paper presents examination of different features and indices for detection of induced epileptic seizures from rat's ECoG signals.
authors.affiliation: Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, P.O. Box 11365-9363, Tehran, Iran. niknazar@alum.sharif.edu
authors.forename: M
authors.initials: M
authors.lastname: Niknazar
authors.forename: S R
authors.initials: SR
authors.lastname: Mousavi
authors.forename: S
authors.initials: S
authors.lastname: Motaghi
authors.forename: A
authors.initials: A
authors.lastname: Dehghani
authors.forename: B
authors.initials: B
authors.lastname: Vosoughi Vahdat
authors.forename: M B
authors.initials: MB
authors.lastname: Shamsollahi
authors.forename: M
authors.initials: M
authors.lastname: Sayyah
authors.forename: S M
authors.initials: SM
authors.lastname: Noorbakhsh
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000503:physiopathology; D003292:Convulsants / Q000633:toxicity; D016208:Databases, Factual / Q000706:statistics & numerical data; D004195:Disease Models, Animal; D018450:Disease Progression; D004569:Electroencephalography; D004827:Epilepsy / Q000139:chemically induced / Q000175:diagnosis*; D010433:Pentylenetetrazole / Q000633:toxicity; D051381:Rats; D012815:Signal Processing, Computer-Assisted
chemical_list: D003292:Convulsants; D010433:Pentylenetetrazole