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Browsing Pósters by Author "D'Giano, Carlos"
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póster.listelement.badge Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters(2018) Zorgno, Ivanna; Blanc, María Cecilia; Oxenford, Simón; Gil Garbagnoli, Francisco; D'Giano, Carlos; Quintero-Rincón, Antonio"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."póster.listelement.badge A new algorithm for automatic identification of spike-wave EEG signals in epileptic patient-specific(2018) Racca, Dora María; Quintero-Rincón, Antonio; Muro, Valeria; D'Giano, Carlos"Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which results from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical processing. In this work we propose a new method to indentify and characterize patient specific spike-and-wave EEG epileptic signals. The method is based on the use of trained neuronal networks on probability density function parameters of the translation and rescaling of the Student'st-distribution (location: µ,scale: σ and shape: ν) of pure spike-and-wave-signals. The neuronal network was trained with both normal and epileptic signals. The study resulted in 100% specificity and sensitivity on the studied signals."póster.listelement.badge Spike-and-wave epileptiform discharge pattern detection based on Kendall’s Tau-b Coefficient(2019) Ems, Joaquín; Hirschson Álvarez Prado, Lourdes; Carenzo, Catalina; Muro, Valeria; D'Giano, Carlos; Quintero-Rincón, Antonio"Epilepsy is a main public health issue. An appropriate epileptiform discharges pattern detection of this neurological disease, is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike-and-wave discharge pattern detection based on Kendall’s Tau Coefficient. The proposed approach is demonstrated on a real dataset containing spike-and-wave discharge signals, where our performance is evaluated in terms of high Specificity, rule in (SpPIn) with 94% for patient-specific spike-and-wave discharge detection and 83% for a general spike-and-wave discharge detection."