Ensemble Machine Learning Based Identification of Adult Epilepsy

Abstract

Epilepsy is a chronic non-communicable illness that affects brain individuals and impacts more than 50 million people globally. To predict epileptic seizures, we proposed machine learning-based ensemble learning technique in this study. In the preprocessed stage, we applied some important techniques such as Power line noise reduction and dividing the record into windows of 5 seconds. The project is created by the help of ensemble machine learning technique, which employs several machine learning algorithms, we used the following algorithms: decision tree, support vector machine, artificial neural networks, and convolutional neural networks. We used a dataset from PhysioNet website that contains adult EEG signals. Several convolutional layers were used to extract features from the EEG signals, after that, the feature set is utilized to train a classifier model, which combines the results. Our approach successfully reached 91% accuracy while 91% sensitivity and 91% specificity, respectively.