@inproceedings{0c2d5d0ef9c04b0b97b0bf8e51712dc1,
title = "Automatic recognition of epileptiform EEG abnormalities",
abstract = "Long term EEG examinations, for example during epilepsy diagnosis, can be performed more efficiently with support of automated abnormality detection. Currently, these methods are usually developed based on one specific database, which limits the possibilities of generalizations. Here, we present a machine learning solution for detection of interictal abnormal EEG segments optimized on the publically available TUH Abnormal EEG Corpus. The classifier is further re-trained and tested on several combinations of publicly available data sets. The results achieved internally on the datasets are comparable to the known state of the art, while training and testing on different sources produced accuracy in the range of 67.51\% to 99.50\%. Lower accuracy is achieved when the training data set is highly preprocessed and relatively small.",
keywords = "EEG, Epilepsy, Interictal abnormality, Spike detection",
author = "Alexander Brenner and Ekaterina Kutafina and Jonas, \{Stephan M.\}",
note = "Publisher Copyright: {\textcopyright} 2018 European Federation for Medical Informatics (EFMI) and IOS Press.; 40th Medical Informatics in Europe Conference, MIE 2018 ; Conference date: 24-04-2018 Through 26-04-2018",
year = "2018",
doi = "10.3233/978-1-61499-852-5-171",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "171--175",
editor = "Adrien Ugon and Daniel Karlsson and Klein, \{Gunnar O.\} and Anne Moen",
booktitle = "Building Continents of Knowledge in Oceans of Data",
}