The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the classifiers and test the results. The goodness of the classification is performed upon the basis of the computation of quality parameters, such as the area under the receiver operating characteristic curve (ROC), the F1 score coefficient and the accuracy. In all the cases the methodology proposed gives high performance results that encourage the line of research of the present work.

En este trabajo se hizo un estudio comparativo de las diferentes alternativas open-source que existen actualmente para el modelado, simulación y optimización de los sistemas eléctricos de potencia. En este caso se evaluaron las librerías PowerModels, Matpower y PFNET, las tres desarrolladas sobre lenguajes de programación diferentes, sobre la base de datos PGLIB.Se describieron los solvers y parámetros seleccionados en cada librería, detallando los resultados obtenidos para cada caso resuelto y para cada alternativa planteada. En base a esto se analizaron los tiempos insumidos y los desvíos con respecto a la línea de base de PGLIB.

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