Predictive Models for Differentiation Between Normal and Abnormal EEG Through Cross-Correlation and Machine Learning Techniques

Currently, in hospitals and medical clinics, large amounts of data are becoming increasingly registered, which usually are derived from clinical examinations and procedures. An example of stored data is the electroencephalogram (EEG), which is of high importance to the various diseases that affect the brain. These data are stored to keep the patient’s clinical history and to help medical experts in performing future procedures, such as pattern discovery from specific diseases. However, the increase in medical data storage makes unfeasible their manual analysis. Also, the EEG can contain patterns difficult to be observed by naked eye. In this work, a cross-correlation technique was applied for feature extraction of a set of 200 EEG segments. Afterwards, predictive models were built using machine learning algorithms such as J48, 1NN, and BP-MLP (backpropagation based on multilayer perceptron), that implement decision tree, nearest neighbor, and artificial neural network, respectively. The models were evaluated using 10-fold cross-validation and contingency table methods. The evaluation results showed that the model built with the J48 performed better and was more likely to correctly classify EEG segments in this study than 1NN and BP-MLP, corresponding to 98.50% accuracy.