Ensemble Modelling for Predicting the Relation between Bio-psychosocial Signals and Seizures using the Gradient Boosting Method

Abstract

In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Ensemble Learnings has become popular research area among the researchers related to the field of automatic classification of electroencephalograph signals for predication of mental health issues like seizures. However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and Ensemble Learnings based EEG signals classification method for seizures using Extreme Gradient Boosting Model.Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method (XGBoost) and Random Forest method. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals. In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy, specificity and sensitivity.