loading
Documents

Research.Publish.Connect.

Paper

Authors: Ludovic Gardy 1 ; Emmanuel Barbeau 2 and Christophe Hurter 3

Affiliations: 1 University of Toulouse, UPS, Centre de Recherche Cerveau et Cognition, Toulouse, France, CNRS, CerCo, Purpan Hospital, Toulouse, France, French Civil Aviation University, ENAC, Avenue Edouard Belin, Toulouse, France ; 2 University of Toulouse, UPS, Centre de Recherche Cerveau et Cognition, Toulouse, France, CNRS, CerCo, Purpan Hospital, Toulouse, France ; 3 French Civil Aviation University, ENAC, Avenue Edouard Belin, Toulouse, France

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Electroencephalography, EEG, Time Series Visualization, Signal Processing, Kernel Density Estimation, Convolution, Noisy Signal, Event Detection, Epilepsy, Accessibility.

Abstract: Analyzing the electroencephalographic (EEG) signal of epileptic patients as part of their diagnosis is a very long and tedious operation. The most common technique used by medical teams is to visualize the raw signal in order to find pathological events such as interictal epileptic spikes (IESs) or abnormal oscillations. More and more efforts are being adopted to try to facilitate the work of doctors by automating this process. Our goal was to analyze signal density fields to improve the visualization and automatic detection of pathological events. We transformed the EEG signal into images on which we applied a convolution filter based on a Kernel Density Estimation (KDE). This method that we propose to call CKDE for Convolutional Kernel Density Estimation allowed the emergence of local density fields leading to a better visualization as well as automatic detection of IESs. Future work will be necessary to make this technique more efficient, but preliminary results are very encouragin g and show a high performance compared to a visual inspection of the data or some other automatic detection techniques. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.235.172.213

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Gardy, L.; Barbeau, E. and Hurter, C. (2020). Automatic Detection of Epileptic Spikes in Intracerebral EEG with Convolutional Kernel Density Estimation.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 101-109. DOI: 10.5220/0008877601010109

@conference{hucapp20,
author={Ludovic Gardy. and Emmanuel J. Barbeau. and Christophe Hurter.},
title={Automatic Detection of Epileptic Spikes in Intracerebral EEG with Convolutional Kernel Density Estimation},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP,},
year={2020},
pages={101-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008877601010109},
isbn={978-989-758-402-2},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP,
TI - Automatic Detection of Epileptic Spikes in Intracerebral EEG with Convolutional Kernel Density Estimation
SN - 978-989-758-402-2
AU - Gardy, L.
AU - Barbeau, E.
AU - Hurter, C.
PY - 2020
SP - 101
EP - 109
DO - 10.5220/0008877601010109

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.