Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters

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"Epilepsy is a disease caused by an excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an ongoing challenge in biomedical signal processing. In this paper, a new method is proposed for onset seizure detection in epileptic EEG sign...

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Detalles Bibliográficos
Autores principales: Zorgno, Ivanna, Blanc, María Cecilia, Oxenford, Simón, Gil Garbagnoli, Francisco, D'Giano, Carlos, Quintero-Rincón, Antonio
Formato: Ponencias en Congresos acceptedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES DIGITALES
EPILEPSIA
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1855
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
Descripción
Sumario:"Epilepsy is a disease caused by an excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an ongoing challenge in biomedical signal processing. In this paper, a new method is proposed for onset seizure detection in epileptic EEG signals based on parameters from the t-location-scale distribution coupled with the variance and the Pearson correlation coefficient. The 1-nearest neighbor classifier achieved a 91% sensitivity (True positive rate) and 95% specificity (True Negative Rate) with a delay of 4.5 seconds (on average) in the 45 signals analyzed, which suggests that the proposed methodology is potentially useful for seizure onset detection in epileptic EEG signals."

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