Abstract:

Epilepsy, a complex neurological disorder characterized by recurrent seizures, affects approximately 65 million people worldwide. The genesis and propagation of epilepsy are intricately tied to the neurological networks within the brain. Understanding these networks could lead to significant advancements in the diagnosis, prognosis, and treatment of epilepsy. This paper explores the application of big data analytics in gaining insights into these neurological networks.
We leverage vast and varied datasets, including neuroimaging data, electroencephalograms (EEGs), and genomic databases, to extract complex patterns and insights. Using machine learning techniques, we identify abnormalities in brain networks that could serve as potential biomarkers for epilepsy. Our analysis also demonstrates that seizure prediction accuracy has improved over time, offering a promising tool for better management of epilepsy. Furthermore, we explore the genomic underpinnings of epilepsy and uncover several genetic variants associated with the disorder.
These findings underscore the immense potential of big data analytics in epilepsy research. Despite existing challenges such as data privacy and the translation of research findings into clinical practice, the application of big data in neurology holds promise for advancing our understanding of epilepsy and other neurological disorders, transforming research, clinical practice, and patient care. Future research directions and further exploration are warranted to fully realize this potential.

Keywords:

EPILEPSY, NEUROLOGICAL NETWORKS, BIG DATA ANALYTICS, MACHINE LEARNING, SEIZURE, PREDICTION, NEUROIMAGING, GENOMIC DATABASES, BIOMARKERS