Fast statistical model-based classification of epileptic EEG signals

"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;...

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Detalles Bibliográficos
Autores principales: Quintero-Rincón, Antonio, Pereyra, Marcelo, D'Giano, Carlos, Risk, Marcelo, Batatia, Hadj
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
EPILEPSIA
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
ALGORITMOS
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1633
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1633
record_format dspace
spelling I32-R138-123456789-16332022-12-07T13:06:39Z Fast statistical model-based classification of epileptic EEG signals Quintero-Rincón, Antonio Pereyra, Marcelo D'Giano, Carlos Risk, Marcelo Batatia, Hadj EPILEPSIA ELECTROENCEFALOGRAFIA PROCESAMIENTO DE SEÑALES ESTADISTICA ALGORITMOS "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 awavelet 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." 2019-06-24T17:59:26Z 2019-06-24T17:59:26Z 2018-01 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 0208-5216 http://ri.itba.edu.ar/handle/123456789/1633 en info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bbe.2018.08.002 info:eu-repo/grantAgreement/EPSRC/EP/D063485/1/UK. Swindon info:eu-repo/grantAgreement/ITBACyT/34/2015/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/FLENI/Protocolo/07/15/AR. Ciudad de Buenos Aires info:eu-repo/grantAgreement/ANID/STICAmSUD/CL. Santiago/DynBrain application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic EPILEPSIA
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
ALGORITMOS
spellingShingle EPILEPSIA
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
ALGORITMOS
Quintero-Rincón, Antonio
Pereyra, Marcelo
D'Giano, Carlos
Risk, Marcelo
Batatia, Hadj
Fast statistical model-based classification of epileptic EEG signals
topic_facet EPILEPSIA
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
ALGORITMOS
description "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 awavelet 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."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Quintero-Rincón, Antonio
Pereyra, Marcelo
D'Giano, Carlos
Risk, Marcelo
Batatia, Hadj
author_facet Quintero-Rincón, Antonio
Pereyra, Marcelo
D'Giano, Carlos
Risk, Marcelo
Batatia, Hadj
author_sort Quintero-Rincón, Antonio
title Fast statistical model-based classification of epileptic EEG signals
title_short Fast statistical model-based classification of epileptic EEG signals
title_full Fast statistical model-based classification of epileptic EEG signals
title_fullStr Fast statistical model-based classification of epileptic EEG signals
title_full_unstemmed Fast statistical model-based classification of epileptic EEG signals
title_sort fast statistical model-based classification of epileptic eeg signals
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1633
work_keys_str_mv AT quinterorinconantonio faststatisticalmodelbasedclassificationofepilepticeegsignals
AT pereyramarcelo faststatisticalmodelbasedclassificationofepilepticeegsignals
AT dgianocarlos faststatisticalmodelbasedclassificationofepilepticeegsignals
AT riskmarcelo faststatisticalmodelbasedclassificationofepilepticeegsignals
AT batatiahadj faststatisticalmodelbasedclassificationofepilepticeegsignals
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