Browsing by Author "Gil Garbagnoli, Francisco"

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    Desarrollo de un alcoholímetro de bajo costo utilizando Arduino
    (2017) Blanc, María Cecilia; Gil Garbagnoli, Francisco; Hasbani, Jonathan Eliel; Mosquera, Valeria; Schröder Langhaeuser, Julia
    "La meta de este proyecto es diseñar y desarrollar un alcoholímetro de bajo costo que tenga una precisión comparable a la de los alcoholímetros comerciales. Para dicho propósito, se implementó un programa en Arduino capaz de relacionar el nivel de tensión obtenida de un sensor con el nivel de alcohol en sangre."
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    Diseño de aplicativo mobile para la mejora y/o recuperación de las capacidades cognitivas
    (2021-06-04) Gil Garbagnoli, Francisco; Rohleder, Matías
    "El objetivo del proyecto fue el diseño de una aplicación mobile para su utilización en tratamientos de rehabilitación / estimulación enfocada en pacientes con discapacidades cognitivas y/o motoras."
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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."