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Fast statistical model-based classification of epileptic EEG signals

Quintero Rincon, Antonio and Pereyra, Marcelo Alejandro and D'Giano, Carlos and Risk, Marcelo and Batatia, Hadj Fast statistical model-based classification of epileptic EEG signals. (2018) Biocybernetics and Biomedical Engineering, 38 (4). 877-889. ISSN 0208-5216

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Official URL: https://doi.org/10.1016/j.bbe.2018.08.002

Abstract

This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.

Item Type:Article
HAL Id:hal-01942293
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Consejo Nacional de Investigaciones Científicas y Técnicas - CONICET (ARGENTINA)
Other partners > Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia - FLENI (ARGENTINA)
Other partners > Heriot-Watt University (UNITED KINGDOM)
Other partners > Instituto Tecnológico de Buenos Aires - ITBA (ARGENTINA)
Laboratory name:
Funders:
Department of Mathematics, University of Bristol - Instituto Tecnológico de Buenos Aires - FLENI - STICAmSUD interna-tional program
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Deposited By: Antonio Quintero-Rincon
Deposited On:03 Dec 2018 09:10

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