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Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

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dc.contributor.author Quintero Rincón, Antonio
dc.contributor.author Muro, Valeria L.
dc.contributor.author D'Giano, Carlos
dc.contributor.author Prendes, Jorge
dc.contributor.author Batatia, Hadj
dc.date.accessioned 2021-05-03T12:43:50Z
dc.date.available 2021-05-03T12:43:50Z
dc.date.issued 2020-10-29
dc.identifier.citation Quintero Rincón, A., Muro, V., D’Giano, C., Prendes, J., Batatia, H., 2020. Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals. Computers 9, 85. es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/450
dc.identifier.uri https://doi.org/10.3390/computers9040085
dc.description.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_ES
dc.language.iso eng es_ES
dc.publisher MDPI es_ES
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Electroencephalography es_ES
dc.subject Electroencefalografía es_ES
dc.subject Epilepsy es_ES
dc.subject Epilepsia es_ES
dc.title Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.type info:eu-repo/semantics/publishedVersion
dc.description.fil Fil: Quintero Rincón, Antonio. Universidad Católica de Buenos Aires; Argentina. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.
dc.description.fil Fil: Muro, Valeria L. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.
dc.description.fil Fil: D'Giano, Jorge. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.
dc.description.fil Fil: Prendes, Jorge. IRIT-INPT-ENSEEIHT; Francia.
dc.description.fil Fil: Batatia, Hadj. Heriot-Watt Universit. MACS School; Emiratos Árabes Unidos.
dc.relation.ispartofVOLUME 9
dc.relation.ispartofNUMBER 4
dc.relation.ispartofPAGINATION 85
dc.relation.ispartofCOUNTRY Suiza
dc.relation.ispartofCITY Basel
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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