Machine Learning for Epilepsy: A Comprehensive Exploration of Novel EEG and MRI Techniques for Seizure Diagnosis

This work focuses on automated epileptic seizure diagnosis (ESD) and prediction (ESP) to clarify the expanding role of machine learning (ML) in epileptic analysis. It outlines the current approaches and challenges in the diagnosis and prognosis of epilepsy and examines the convergence of magnetic resonance imaging (MRI), electroencephalogram (EEG), and ML.

This paper lists current methods for segmentation, localization, feature extraction, diagnosis, and prognosis after providing a brief medical review to distinguish between different forms of epilepsy. A particular focus is on using ML to EEG and MRI data, describing classification techniques to differentiate normal and epileptic activity.

We highlight the potential of ML-driven methods for computer-aided epilepsy diagnosis and prognosis. We discuss achievements, challenges, and future directions, including devising novel techniques for automated alerts and seizure frequency estimation with minimal computational burden.

ML interfaces offer new possibilities for real-time seizure diagnosis in refractory epilepsy patients through wearables and implants. This discovery opens the door for improved diagnostic precision and individualized treatment plans in this field by using ML’s capabilities.

The graphical abstract presents the machine Learning (ML) workflow for epileptic seizure diagnosis (ES) in detail. It begins with collecting data, such as magnetic resonance imaging (MRI) and electroencephalogram (EEG) data. Subsequently, features were extracted from the MRI and EEG data and used to train and evaluate machine learning models. The trained models were then applied to ES classification. Finally, ML algorithms proved to have the potential to revolutionize the diagnosis and treatment of epilepsy. By enabling early detection and personalized treatment, ML algorithms can help improve patient outcomes and quality of life.