Explainable artificial intelligence in epilepsy management: Unveiling the model interpretability
EEG; Feature importance; LIME; Machine learning; Seizure classification; SHAP; XAI
The field of epileptic seizure classification has witnessed significant advancements in the use of electroencephalogram (EEG) data for accurate and timely diagnoses. This study introduces a comprehensive framework for EEG-based seizure classification, encompassing data preprocessing and the application of machine learning techniques, specifically the supervised learning classifier known as Extreme Gradient Boosting (Xgboost). Machine learning methods have shown promising accuracy in binary classification tasks, particularly in distinguishing between seizure and healthy EEG signals. However, the need for a robust explanation of these results and decision-making processes is imperative for technical verification and clinical validation, especially for potential clinical applications. Explainable Artificial Intelligence (XAI) emerges as a critical component in addressing this need. In this chapter, we propose and discuss a binary classification model that leverages Xgboost to classify EEG signals as either Seizure or normal, a crucial aspect in epilepsy diagnosis. XAI techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) are incorporated to elucidate the model's predictions. LIME offers localized interpretability by creating surrogate models for individual predictions, revealing the essential EEG features influencing each classification decision. Conversely, SHAP provides a global perspective on feature importance, shedding light on the collective impact of EEG features on classification outcomes. The synergy between LIME and SHAP enhances our understanding of the model's predictions and the intricate nuances within EEG data. This research highlights the transformative potential of LIME and SHAP in EEG-based seizure classification. The integration of XAI techniques not only enhances the transparency and interpretability of the model but also empowers clinicians and researchers to make more informed decisions, ultimately improving patient care and outcomes in epilepsy management. By bridging the gap between complex EEG data and actionable insights, this study marks a significant paradigm shift in the application of XAI techniques in medical diagnostics. It paves the way for a new era in epilepsy diagnosis and management, where advanced machine learning models guided by LIME and SHAP play a crucial role in revolutionizing healthcare practices. 2025 Elsevier Inc. All rights reserved.
Najmusseher; Nizar Banu, P.K.; Azar, Ahmad Taher; Kamal, Nashwa Ahmad
Explainable AI in Healthcare Imaging for Medical Diagnoses: Digital Revolution of Artificial Intelligence;pp.175-211
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-23979-3.00008-7 https://www.scopus.com/pages/publications/105011234107?origin=resultslist
Najmusseher, Department of Computer Science, CHRIST (Deemed to be University), Central Campus, Karnataka, Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Central Campus, Karnataka, Bangalore, India; Azar A.T., College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia, Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt; Kamal N.A., Faculty of Engineering, Cairo University, Giza, Egypt