Evaluation of pain perception with objective prediction and deep learning models using EEG signals

Pain is defined as a non-pleasurable, complex and multidimensional sensation experienced by people. Objective classification of pain is of great importance for patients who are monitored in hospitals, for individuals who cannot express themselves, and for people who have been exposed to anesthesia during or after surgery. In addition, subjective grading of pain can cause a misleading response from person to person and may result in the person being directed to the wrong treatment protocol. Thanks to the grading of pain with artificial intelligence models, it will be possible to reach information about orientation to the right treatment and grading the severity of pain of individuals who cannot express themselves. In the thesis study, EEG data obtained from individuals by giving pain stimulus was classified as high and low pain by creating a one-dimensional convolutional neural network model (1 Dimensional Convolutional Neural Networks, 1D CNN). The EEG data obtained for the feature selection to be used in the model were cleared of noise beforehand. In the next step, T-test was applied to the cleaned data and the parts that made a difference were determined. The features in the time-frequency response from the determined sections were extracted and used in the prepared model. The designed model was compared with the Reccurrent Neural Network (RNN) model, which is one of the deep learning models for comparison purposes. As a result of the thesis study, it was observed that the 1D convolutional neural network model, designed within the scope of the information received, made an average of 92% correct classification. The findings show that the model designed for objective grading of pain sensation can be a good marker.