EEG Graph-Based Disease Analysis: A Novel Approach to Neurological Disorder Diagnosis

EEG use in clinical neurophysiology is a keystone for diagnosing neurological disorders. This paper highlights studies that have been done on the application of machine learning to diagnose three common neurological conditions through EEG signals; epilepsy, stroke and migraine. We looked at different researches to determine the specificity of EEG signals in these diseases and the efficiency of various techniques. The results show that machine learning models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) greatly increase diagnostic accuracy. CNN had the best performance metrics among all models for both migraine and stroke diagnoses while SVM was superior in detection of epilepsy. When multiple diagnostic markers from EEG signals are combined, sensitivity and specificity improve for these neurological conditions detection. This comprehensive review underscores the potential of EEG-based machine learning techniques as a supplementary method for accurate diagnosis and monitoring of migraine, epilepsy, and stroke, providing a robust framework for future research and clinical practice. [1] [2]