Detection of epileptic seizures in EEG by using machine learning techniques

In this research a public dataset of recordings of EEG signals of healthy subjects and epileptic patients was used to build three simple classifiers with low time complexity, these are decision tree, random forest and AdaBoost algorithm. The data was initially preprocessed to extract short waves of electrical signals representing brain activity. The signals are then used for the selected models. Experimental results showed that random forest achieved the best accuracy of detection of the presence/absence of epileptic seizure in the EEG signals at 97.23% followed by decision tree with accuracy of 96.93%. The least performing algorithm was the AdaBoost scoring accuracy of 87.23%. Further, the AUC scores were 99% for decision tree, 99.9% for random forest and 95.6% for AdaBoost. These results are comparable to state-of-the-art classifiers which have higher time complexity.