Guo, L., Rivero, D., Dorado, J., Rabunal, J.R. and Pazos, A. (2010) Automatic Epileptic Seizure Detection in EEG Based on Line Length Feature and Artificial Neural Network. Journal of Neuroscience Methods, 191, 101-109.
https://doi.org/10.1016/j.jneumeth.2010.05.020

has been cited by the following article:

• TITLE: An Efficient Method for Epileptic Seizure Detection in Long-Term EEG Recordings

  AUTHORS: Alaa Eldeen Mahmoud Helal, Ahmed Farag Seddik, Mohammed Ali Eldosoky, Ayat Allah Farouk Hussein

  KEYWORDS: Electroencephalogram (EEG), Seizure Detection, Epilepsy Diagnosis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.7 No.12, October 14, 2014

  ABSTRACT: Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.