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Authors: Leonardo Duque Muñoz 1 ; Carlos Guerrero-Mosquera 2 and German Castellanos-Dominguez 2

Affiliations: 1 Instituto Tecnológico Metropolitano, Colombia ; 2 Universidad Nacional de Colombia, Colombia

ISBN: 978-989-8565-36-5

Keyword(s): Rhythms Decomposition, Seizure Detection, Feature Extractions, EEG Classification.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detection and Identification ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: This work introduces a new methodology to select EEG channels related to epileptic seizures by electroencephalogram (EEG) rhythms extraction. Rhythms extraction is an alternative to extract useful information from specific band frequencies, analyze changes in the EEG signals, and detect brain abnormalities. In this approach, the EEG signals are modeled by Exponentially Damped Sinusoidal model (EDS) and the EEG rhythms extraction is based on Stochastic Relevance Analysis (SRA). Achieve results show that EDS model combined with a stochastic relevance measure is a proper alternative for EEG classification of epileptic signals and also could be used for EEG channel selection with seizure activity. The effectiveness of this approach is compared in each experiment with other well known method for feature extraction called as Rhythmic Component Extraction (RCE). This comparison was done based on the performance of the k-NN classifiers and the channels selected were validated by visual inspec tion and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. We conclude that the proposed scheme is a suitable approach for automatic seizure detection at a moderate computational cost, also opening the possibility of formulating new criteria to select, classify or analyze abnormal EEGs channels. (More)

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Paper citation in several formats:
Duque Muñoz, L.; Guerrero-Mosquera, C. and Castellanos-Dominguez, G. (2013). A Channel Selection Method for EEG Classification based on Exponentially Damped Sinusoidal Model and Stochastic Relevance Analysis.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 284-289. DOI: 10.5220/0004196802840289

@conference{biosignals13,
author={Leonardo Duque Muñoz. and Carlos Guerrero{-}Mosquera. and German Castellanos{-}Dominguez.},
title={A Channel Selection Method for EEG Classification based on Exponentially Damped Sinusoidal Model and Stochastic Relevance Analysis},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={284-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004196802840289},
isbn={978-989-8565-36-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - A Channel Selection Method for EEG Classification based on Exponentially Damped Sinusoidal Model and Stochastic Relevance Analysis
SN - 978-989-8565-36-5
AU - Duque Muñoz, L.
AU - Guerrero-Mosquera, C.
AU - Castellanos-Dominguez, G.
PY - 2013
SP - 284
EP - 289
DO - 10.5220/0004196802840289

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