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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Quintero-Rincón, Antonio | es |
dc.contributor.author | Muro, Valeria | es |
dc.contributor.author | D’Giano, Carlos | es |
dc.contributor.author | Prendes, Jorge | es |
dc.contributor.author | Batatia, Hadj | es |
dc.date.accessioned | 2020-11-25T15:35:57Z | - |
dc.date.available | 2020-11-25T15:35:57Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Quintero-Rincón, A., Muro, V., D’Giano, C., Prendes, J., Batatia, H. Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals [en línea]. 2020, Computers, 9 (4). Disponible en: https://repositorio.uca.edu.ar/handle/123456789/10947 | es |
dc.identifier.issn | 2073-431X (online) | - |
dc.identifier.uri | https://repositorio.uca.edu.ar/handle/123456789/10947 | - |
dc.description.abstract | Abstract: Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.rights | Acceso abierto | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.source | Computers Vol.9, No.4, 2020 | es |
dc.subject | EPILEPSIA | es |
dc.subject | ELECTROENCEFALOGRAFIA | es |
dc.subject | ONDAS ENCEFALICAS | es |
dc.subject | TECNICAS DE DIAGNOSTICO NEUROLOGICO | es |
dc.title | Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals | es |
dc.type | Artículo | es |
uca.disciplina | MEDICINA | es |
uca.issnrd | 1 | es |
uca.affiliation | Fil: Quintero-Rincón, Antonio. Pontificia Universidad Católica Argentina; Argentina | es |
uca.affiliation | Fil: Quintero-Rincón, Antonio. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina | es |
uca.affiliation | Fil: Muro, Valeria. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina | es |
uca.affiliation | Fil: D’Giano, Carlos. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina | es |
uca.affiliation | Fil: Prendes, Jorge. Université Toulouse. Institut de Recherche en Informatique; Francia | es |
uca.affiliation | Fil: Batatia, Hadj. Universidad Heriot-Watt de Dubái; Emiratos Árabes Unidos | es |
uca.version | publishedVersion | es |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Facultad de Ingeniería y Ciencias Agrarias | - |
crisitem.author.parentorg | Pontificia Universidad Católica Argentina | - |
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