@inproceedings{6841c650ccbf44c7a9784c7901936e57,
title = "An IFS-Based Similarity Measure to Index Electroencephalograms",
abstract = "EEG is a very useful neurological diagnosis tool, inasmuch as the EEG exam is easy to perform and relatively cheap. However, it generates large amounts of data, not easily interpreted by a clinician. Several methods have been tried to automate the interpretation of EEG recordings. However, their results are hard to compare since they are tested on different datasets. This means a benchmark database of EEG data is required. However, for such a database to be useful, we have to solve the problem of retrieving information from the stored EEGs without having to tag each and every EEG sequence stored in the database (which can be a very time-consuming and error-prone process). In this paper, we present a similarity measure, based on iterated function systems, to index EEGs.",
author = "Ghita Berrada and {de Keijzer}, Ander",
year = "2011",
doi = "10.1007/978-3-642-20847-8_38",
language = "English",
isbn = "978-3-642-20846-1",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "457--468",
editor = "Huang, {Joshua Zhexue} and Longbing Cao and Jaideep Srivastava",
booktitle = "Advances in Knowledge Discovery and Data Mining",
address = "United Kingdom",
}