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

Zorgno, Ivanna and Blanc, María Cecilia and Oxenford, Simon and Gil Garbagnoli, Francisco and D'Giano, Carlos and Quintero-Rincón, Antonio Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters. (2019) In: 2018 IEEE Biennial Congress of Argentina (ARGENCON), 6 June 2018 - 8 June 2018 (San Miguel de Tucumán, Argentina).

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Official URL: http://doi.org/10.1109/ARGENCON.2018.8646234


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

Item Type:Conference or Workshop Item (Paper)
Additional Information:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-02401694
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia - FLENI (ARGENTINA)
Other partners > Instituto Tecnológico de Buenos Aires - ITBA (ARGENTINA)
Deposited On:28 Nov 2019 14:40

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