Propuesta de sistema para el diagnóstico de epilepsia con técnicas de clasificación

Abstract

Epilepsy is a brain abnormality that leads its patients to suffer from seizures, which conditions their behavior and lifestyle. Neurologists use an electroencephalogram (EEG) to diagnose this disease. This test illustrates the signaling behavior of a person's brain, allowing, among other things, the diagnosis of epilepsy. From a visual analysis of these signals, neurologists identify patterns such as peaks or valleys, looking for any indication of a brain disorder that leads to the diagnosis of epilepsy, in a purely qualitative way. However, by applying an analysis based on Fourier signal analysis through rapid transformation in the frequency domain, patterns can be identified quantitatively to differentiate between patients diagnosed with the disease and others who are not. In this article an analysis of the EEG signal is performed to extract characteristics in patients already classified as epileptic and non-epileptic, which will be used in the training of models based on classification techniques such as logistic regression, neural networks and vector support machines. Based on the results obtained with each technique, an analysis was performed to decide which of these three behaves better