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dc.contributor.author
Quintero Rincón, Antonio
dc.contributor.author
Pereyra, Marcelo
dc.contributor.author
D'Giano, Carlos
dc.contributor.author
Risk, Marcelo
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dc.contributor.author
Batatia, Hadj
dc.date.available
2020-03-04T19:57:25Z
dc.date.issued
2018-01
dc.identifier.citation
Quintero Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Risk, Marcelo; Batatia, Hadj; Fast statistical model-based classification of epileptic EEG signals; Elsevier; Biocybernetics And Biomedical Engineering; 38; 4; 1-2018; 877-889
dc.identifier.issn
0208-5216
dc.identifier.uri
http://hdl.handle.net/11336/98797
dc.description.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
EEG
dc.subject
EPILEPSY
dc.subject
GENERALIZED GAUSSIAN DISTRIBUTION
dc.subject
LEAVE-ONE-OUT CROSS-VALIDATION
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LINEAR CLASSIFIER
dc.subject
WAVELET FILTER BANKS
dc.subject.classification
Otras Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Fast statistical model-based classification of epileptic EEG signals
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-03-03T15:05:44Z
dc.journal.volume
38
dc.journal.number
4
dc.journal.pagination
877-889
dc.journal.pais
Países Bajos
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dc.description.fil
Fil: Quintero Rincón, Antonio. Instituto Tecnologico de Buenos Aires. Departamento de Bioingenieria; Argentina
dc.description.fil
Fil: Pereyra, Marcelo. Heriot Watt University; Reino Unido
dc.description.fil
Fil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
dc.description.fil
Fil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnologico de Buenos Aires. Departamento de Bioingenieria; Argentina
dc.description.fil
Fil: Batatia, Hadj. University of Toulouse; Francia
dc.journal.title
Biocybernetics And Biomedical Engineering
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0208521618301219
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.bbe.2018.08.002
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