Browsing by Author "Legnani, W. E."

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    Análisis matemático de potenciales auditivos evocados de tronco encefálico (BERA)
    (2004-10-01) Legnani, W. E.; Curcio, V.; Hayes, A.L.; Ibañez, F.; Rodríguez Sueldo, X.
    El potencial auditivo evocado de tronco encefálico BERA surge al registrar las respuestas eléctricas desencadenadas en los nervios auditivos en los 10 milisegundos posteriores a haberse producido el estimulo. En el presente trabajo se aplicaron herramientas matemáticas basadas en la transformada wavelet para sistematizar el análisis de los mismos en un marco de formalización que permita realizar diagnósticos mas precisos y que contribuya a obtener mayor información a partir de los datos recabados con dicho estudio.
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    Application of signal classifiers in auditory evoked potentials for the detection of pathological patients
    (2023-01-01) Baldiviezo, M. G.; Barbería, J.L.; Bontempo, C. B.; Corsaro, Y.; Fernández Biancardi, F.; Hernando, M. R.; Licata Caruso, L.; Paglia, A.; Rodríguez, M. R.; Legnani, W. E.
    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
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    ECG signals classification using overlapping variables to detect atrial fibrillation
    (2022-03-01) Ziccardi, I. G.; Rey, A. A.; Legnani, W. E.
    In the present work a method for the detection of the cardiac pathology known as atrial fibrillation is proposed by calculating different information, statistics and other nonlinear measures over ECG signals. The original database contains records corresponding to patients who are diagnosed with this disease as well as healthy subjects. To formulate the dataset the R´enyi permutation entropy, Fisher information measure, statistical complexity, Lyapunov exponent and fractal dimension were calculated, in order to determine how to combine this features to optimize the identification of the signals coming from ECG with the above mentioned cardiac pathology. With the aim to improve the results obtained in previous studies, a classification method based upon decision trees algorithms is implemented. Later a Montecarlo simulation of one thousand trials is performed with a seventy percent randomly selected from the dataset dedicated to train the classifier and the remaining thirty percent reserved to test in every trial. The quality of the classification is assessed through the computation of the area under the receiver operation characteristic curve (ROC), the F1-score and other classical performance metrics, such as the balanced accuracy, sensitivity, specificity, positive and negative predicted values. The results show that the incorporation of all these features to the dataset when are employed to train the classifier in the training task produces the best classification, in such a way that the largest quality parameter is achieved.