Process design and data-driven decision-making for brain health management : a machine learning approach for epilepsy seizure classification

Abstract

The present study focuses on utilizing visualization techniques and sophisticated machine learning methods in digital care pathways for strengthening the categorization of epileptic episodes using electroencephalogram (EEG) signal information. Over fifty million people globally are affected by epilepsy, a prevalent neurological disease that places a heavy cost on healthcare systems. For this reason, accurate diagnostic tools are very necessary to provide optimal epilepsy care. This study aims to establish a comprehensive approach that utilizes the techniques of machine learning, such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) with visual analytics, for enhancing the categorization of epileptic seizures. Additionally, it facilitates a comprehensive evaluation of brain health by integrating cognitive abilities, brain imaging information, and factors associated with lifestyle.

The main goal of this research is to develop visual analytics for decision-making and design processes to improve seizure detection accuracy by examining the distinctive features of non-seizure EEG episodes (brain tumor area, healthy part, eye closed, and eye open) and comparing these classes to seizure episodes. Binary classifiers are trained to evaluate the seizure class against each non- seizure class. The research study evaluates the consequences of incorporating non-seizure situations, namely seizure vs (eyes open and eyes closed) and seizure vs (tumor region and healthy region), and predicts tumors from brain healthy class on machine learning classification effectiveness. The study seeks to improve seizure detection and tumor accuracy by incorporating non-seizure EEG categories and examining its findings with those associated with classes.

An interactive visual analytics dashboard is developed to guide and support medical practitioners in informed decision-making by displaying key metrics related to brain health, including cognitive function scores, imaging data, and lifestyle factors. The findings of this research contribute to personalized medication and advanced planning for epilepsy treatment, with the potential to significantly enhance patient health outcomes.