Department of Computer Systems

Conference paper

KEŠNER Filip, CIMBÁLNÍK Jan, DOLEŽALOVÁ Irena, BRÁZDIL Milan and SEKANINA Lukáš. Fast Automated Interictal Spike Detection in iEEG/ECoG Recordings. In: Proceedings of NEUROTECHNIX: International Congress on Neurotechnology, Electronics and Informatics. Lisabon, 2015, pp. 1-4.
Publication language:english
Original title:Fast Automated Interictal Spike Detection in iEEG/ECoG Recordings
Pages:1-4
Proceedings:Proceedings of NEUROTECHNIX: International Congress on Neurotechnology, Electronics and Informatics
Conference:NEUROTECHNIX
Place:Lisabon, PT
Year:2015
Annotation
MOTIVATION
Interictal spikes have been established as an im-
portant biomarker in surface EEG and intracranial
iEEG recordings for some time (Staley et al., 2011).
Spikes are used for clinical practice and research of
epilepsy, ADHD and also in other areas(Barkmeier
et al., 2012a). Although gold standard for interictal
spike detection has been and still mainly is manual
evaluation, it has been shown that higher consistency
of results can be achieved by automated detection al-
gorithm (Barkmeier et al., 2012b) also saving enor-
mous amount of work for reviewers thus providing
faster data analysis for research or even clinical prac-
tice.

OBJECTIVES
Computational efficiency is not so important, when
recordings from only few channels are processed and
real-time detection is not necessary. Example of those
would be recordings from rodents(Ovchinnikov et al.,
2010). However, when processing intracranial record-
ings from humans, in as much as 150 channels with 5
KHz sampling rate, which are in average 30 minutes
long, computational time requirements gain great deal
of importance. This algorithm has been designed to
address this very issue. While several terabytes(just
our institution) of such recordings are ready for pro-
cessing, detection algorithm must have been designed
to allow fast offline processing of intracranial record-
ings or even real-time detection in at least hundreds
of channels simultaneously. In order to process large
signal data, memory access is crucial bottleneck for
CPU processing, which puts high requirements on ef-
fective cache utilization, reducing frequency of access
to RAM.
BibTeX:
@INPROCEEDINGS{
   author = {Filip Ke{\v{s}}ner and Jan Cimb{\'{a}}ln{\'{i}}k and Irena
	Dole{\v{z}}alov{\'{a}} and Milan Br{\'{a}}zdil and
	Luk{\'{a}}{\v{s}} Sekanina},
   title = {Fast Automated Interictal Spike Detection in iEEG/ECoG
	Recordings},
   pages = {1--4},
   booktitle = {Proceedings of NEUROTECHNIX: International Congress on
	Neurotechnology, Electronics and Informatics},
   year = {2015},
   location = {Lisabon, PT},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en.iso-8859-2?id=10944}
}

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