Prediction of patients with idiopathic generalized epilepsy from healthy controls using machine learning from scalp EEG recordings.

Epilepsy detection is essential for patients with epilepsy and their families, as well as for researchers and medical staff. The use of electroencephalogram (EEG) as a tool to support the diagnosis of patients with epilepsy is fundamental. Today, machine learning (ML) techniques are widely applied in neuroscience. The main objective of our study is to differentiate patients with idiopathic generalized epilepsy from healthy controls by applying machine learning techniques on interictal electroencephalographic recordings. Our research predicts which patients have idiopathic generalized epilepsy from a scalp EEG study. In addition, this study focuses on using the extreme gradient boosting (XGB) method applied to scalp EEG. XGB is one of the variants of gradient boosting and is a supervised learning algorithm. This type of system is developed to increase performance and processing speed. Through this proposed method, an attempt is made to recognize patterns from scalp EEG recordings that would allow the detection of IGE with high accuracy and differentiate IGE patients from healthy controls, creating an additional tool to support clinicians in their decision-making. Among the ML methods applied, the proposed XGB method achieves a better prediction of the distinct features in EEG signals from patients with IGE. XGB was 6.26% more accurate than the k-Nearest Neighbours method and was more accurate than the support vector machine (10.61%), decision tree (9.71%) and Gaussian Naïve Bayes (11.83%). Besides, the proposed XGB method showed the highest area under the curve (AUC 98%) and balanced accuracy (98.13%) of all methods tested. Application of ML technique in EEG of patients with epilepsy is very recent and is emerging with promising results. In this research work, we showed the usefulness of ML techniques to identify and predict generalized epilepsy from healthy controls in scalp EEG studies. These findings could help develop automated tools that integrate these ML techniques to assist clinicians in differentiating between patients with IGE from healthy controls in daily practice.