EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks

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 brain disorder that leads to the diagnosis of epilepsy in a purely qualitative way. However, by applying a test based on Fourier signal analysis through rapid transformation in the frequency domain, patterns can be quantitatively identified to differentiate 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, artificial neural networks, support vector machines, and convolutional neural networks. Based on the results obtained with each technique, an analysis is performed to decide which of these behaves better. In this study traditional classification techniques were implemented that had as data frequency data in the channels with distinctive information of EEG examinations, this was done through a feature extraction obtained with Fourier analysis considering frequency bands. The techniques used for classification were implemented in Python and through a comparison of metrics and performance, it was concluded that the best classification technique to characterize epileptic patients are artificial neural networks with an accuracy of 86%.

Principal Components Analysis of EEG Signals for Epileptic Patient Identification

“…To observe the feasibility of using APC, patient classification is performed using logistic regression. This work can be considered as a step to improve the classification shown in [5] using different classification techniques.…”

“…Regarding EEG frequency features analysis, in [5], several signals in time and frequency domains for epileptic and non-epileptic patients can be observed, where the values of P i for different channels are calculated, considering, as non-epileptic patient example, a 75-year-old man (checking the mental status) and, for epileptic patient, a 75-year-old woman (with urinary incontinence).…”

“…The respective transformation can be expressed using Equation (5) where W is the transformation (projection) matrix composed by elements φ ji .…”

“…From these results, there is a tendency to present a lower MSE value with the validation data. Finally, Table 6 shows a comparison (for the bests cases) with the models developed in [5] where the classification is carried out without PCA. This table shows the implementation with logistic regression with and without PCA, as well as the implementation with artificial neural networks and vector support machines.…”

“…On the basis of this evidence, 20 epileptic and 20 nonepileptic patients were selected, between the ages of 19 and 81 years, with average age of 53 years. In this subset, there are 22 women and 18 men; in this order, Table 1 describes the ages and gender of the samples that are part of the data set [5].…”