Browsing by Author "Zorgno, Ivanna"

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    Aplicación de vapor para el control de los huevos del mosquito Aedes aegypti
    (2020-11-12) Blanc, María Cecilia; Zorgno, Ivanna; Fernández, María Laura
    "El mosquito Aedes aegypti es un vector de hábitat domiciliario transmisor de enfermedades como el dengue, fiebre amarilla, zika y chikunguña. Es posible evitar la transmisión de estas enfermedades utilizando métodos de control del vector. Estudios previos han demostrado que el agua caliente puede disminuir la viabilidad de los de los huevos de Aedes aegypti y el vapor de agua, generar la mortalidad de los huevos y larvas de Aedes albopictus y Culex quinquefasciatus. En base a estos resultados, en el presente trabajo se analiza el efecto del vapor de agua sobre la viabilidad de los huevos de Aedes aegypti ya que este medio presenta una transferencia de calor más eficiente."
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    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."
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    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."