Analysis of multi-channell iEEG signals with convolutional neural networks

Since EEG contains information about brain activity, and the brain is also the central decision organ of a person, its analysis is of particular importance. Most of the neural activity takes place in the brain. As much as we understand the activities in the brain, concepts such as consciousness, memory, and intelligence are better understood and interpreted. Within this context, the importance of the brain is increasing every day. Electroencephalogram (EEG) refers to the measurement of signals from the skull formed due to the activation of neurons in the brain. iEEG is a type of EEG, but iEEG refers to measurement directly from the surface of the brain. Since this is a multi-channel measurement, it leads to an even more precise perception of information reaching the brain's surface. However, this means a data crowd, as more parameters are generated. In this study, in order to contribute to the development of the diagnosis and early diagnosis systems of epileptic intracranial EEG (iEEG) signals before seizure (preictal), during seizure (ictal) and after seizure (postictal), brain dynamics and to reach results that will form the basis for the detection of pathological conditions of IEEG signals. Convolutional Neural Networks (CNN) method was applied to low frequency scalograms. Within the scope of this study, IEEG data obtained from epileptic subjects was first decomposed into subbands by wavelet transform, and then Shannon entropy was applied to these five subbands (delta, theta, alpha, beta and gamma). From the results obtained, it was seen that the delta subband entropy value was lower than the other subband entropy values. A lower entropy value means that the data is less disordered and chaotic. Less disorganized and chaotic data means better predictability. In this context, scalogram images of the low-frequency delta subband before, during and after seizure were obtained and trained with the CNN method, and an accuracy rate of 93.33% was obtained in the test.