Integrating (OLAP) Online Analytical Processing With a Fast Algorithm for Epileptic Seizures Detection

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures, significantly impacting the quality of life of patients. Effective detection and analysis of epileptic seizures are crucial for timely intervention and management. This paper presents the integration of a fast algorithm for epileptic seizures detection with an OLAP (Online Analytical Processing) system using a star schema data warehouse model. By leveraging OLAP capabilities, we perform multi-dimensional analyses to derive insights into patient-specific seizure patterns, channel efficacy, and temporal trends. The CHB-MIT Scalp EEG Database is utilized for this study, demonstrating the practical application of the proposed model. The results highlight the potential of combining advanced algorithms with OLAP for enhanced data analysis and improved patient care.