Transfer learning for epilepsy detection using spectrogram images

Abstract

<span>Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.</span>

Mobilenet, inception ResNet and GoogleNet for epilepsy detection using spectrogram images

“…The dataset was obtained from the American University of Beirut in the Epilepsy Monitoring Unit, with EEG signal recording using 21 scalp electrodes arranged in a 10-20 system [17]. We started by obtaining the spectrogram images from the EEG EDF file for using it as input to the 2D model, which has been used to extract features that will benefit the different classifiers to succeed in the classification normal and epileptic spectrogram [18] as it is shown in Figure 2. The Dataset includes after extracting from EDF file the spectogram images, they are 1,024×1,007 pixels.…”