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Gardner, A.B., Krieger, A.M., Vachtsevanos, G. and Litt, B. (2006) One-class novelty detection for seizure analysis from intracranial EEG. Journal of Machine Learning Research, 7, 1025-1044.

has been cited by the following article:

• TITLE: A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection

  AUTHORS: Yuedong Song

  KEYWORDS: Electroencephalogram; Epileptic Seizure; Automatic Diagnostic Systems; Feature Analysis; Recognition

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.12, December 9, 2011

  ABSTRACT: Epilepsy is one of the most common neurological disorders-approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. Until now, reviews of epileptic seizure detection have been published but none of them has specifically reviewed developments of automatic medical support systems utilized for EEG-based epileptic seizure detection. This review aims at filling this lack. The main objective of this review will be to briefly discuss different methods used in this research field and describe their critical properties.