Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters
Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters
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Fecha
2018
Autores
Zorgno, Ivanna
Blanc, María Cecilia
Oxenford, Simón
Gil Garbagnoli, Francisco
D'Giano, Carlos
Quintero-Rincón, Antonio
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Resumen
"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."