A Comparative Analysis of Classifier Performance for Epileptic Seizure Detection Using EEG Signals

In middle and low-income countries, epilepsy remains undiagnosed in many instances because of an insufficient number of medical specialists and expensive EEG recording devices. In previous studies, many machine learning (ML) based methods were proposed to investigate and classify the EEG signals. However, little work has been performed with EEG data recorded with consumer-grade devices. The extraction of the most discriminating set of features and high misclassification rate is another challenge. To address these problems, this study empirically investigates several data segment sizes and chooses the optimal window size to segment the Guinea-Bissau dataset. Several statistical and spectral feature extraction methods were investigated to obtain useful sets of features from segmented epochs in combination with conventional ML algorithms and ensemble methods. The proposed framework is then implemented on a comparable dataset collected from Nigeria to validate the reliability of the framework. A comparative analysis is performed with conventional ML models and with existing techniques to prove the effectiveness of the proposed methodology. The obtained results demonstrate that XGBoost and LightGBM achieved the highest levels of performance in terms of F1 score and AUC.