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Fast Automated Interictal Spike Detection in iEEG/ECoG Recordings - Using Optimized Memory Access

Topics: EEG and EMG Signal Processing and Applications; Electrical and Magnetic Recordings; Neural Signal Processing; Real Time Monitoringof Neuromuscular and Neural Activity

Authors: Filip Kesner 1 ; Jan Cimbalnik 2 ; Irena Dolezalova 3 ; Milan Brazdil 3 and Lukáš Sekanina 4

Affiliations: 1 Brno University of Technology, Faculty of Information Technology, International Clinical Research Center,Center of Biomedical Engineering and St. Annes University Hospital, Czech Republic ; 2 International Clinical Research Center,Center of Biomedical Engineering and St. Annes University Hospital, Czech Republic ; 3 1st Department of Neurology, St. Annes University Hospital and Medical Faculty and Masaryk University, Czech Republic ; 4 Brno University of Technology and Faculty of Information Technology, Czech Republic

Keyword(s): iEEG, ECoG, Spike Detection, Signal Processing, Epilepsy.

Related Ontology Subjects/Areas/Topics: Electrical and Magnetic Recordings ; EMG Signal Processing and Applications ; Health Engineering and Technology Applications ; Neural Signal Processing ; NeuroSensing and Diagnosis ; Neurotechnology, Electronics and Informatics

Abstract: MOTIVATION Interictal spikes have been established as an important 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 the 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 algorithm (Barkmeier et al., 2012b). Detection algorithms can save enormous amount of work for reviewers and provide a faster data analysis for research or even clinical practice. OBJECTIVES Computational efficiency is not so important when recordings are processed from only a few channels and a real-time detection is not necessary. Example of those would be recordings from rodents (Ovchinnikov et al., 2010). However, when processing intracranial recordings from humans, in as much as 150 channels with 5 kHz samp ling rate, which are in average 30 minutes long, computational time requirements gain a great deal of importance. While several terabytes (just our institution) of such recordings are available for processing, a detection algorithm has to be designed to allow fast offline processing of intracranial recordings or even a real-time detection over at least hundreds of channels simultaneously. In order to process large signal data, the memory access is often crucial bottleneck for CPU processing, which puts high requirements on effective cache utilization, to reduce the access to a slow main memory. The goal of this paper is to propose an efficient spike detection algorithm, particularly, the first level detector. (More)

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Paper citation in several formats:
Kesner, F.; Cimbalnik, J.; Dolezalova, I.; Brazdil, M. and Sekanina, L. (2015). Fast Automated Interictal Spike Detection in iEEG/ECoG Recordings - Using Optimized Memory Access.In - NEUROTECHNIX, ISBN , pages 0-0

@conference{neurotechnix15,
author={Filip Kesner. and Jan Cimbalnik. and Irena Dolezalova. and Milan Brazdil. and Lukáš Sekanina.},
title={Fast Automated Interictal Spike Detection in iEEG/ECoG Recordings - Using Optimized Memory Access},
booktitle={ - NEUROTECHNIX,},
year={2015},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}

TY - CONF

JO - - NEUROTECHNIX,
TI - Fast Automated Interictal Spike Detection in iEEG/ECoG Recordings - Using Optimized Memory Access
SN -
AU - Kesner, F.
AU - Cimbalnik, J.
AU - Dolezalova, I.
AU - Brazdil, M.
AU - Sekanina, L.
PY - 2015
SP - 0
EP - 0
DO -

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