An automated approach for electroencephalography-based seizure detection using machine learning algorithms

Abstract

Epilepsy is a chronic neurological disease that causes recurrent life-threatening seizures due to irregular brain activity. The purpose of seizure detection algorithms is to detect seizures from electroencephalography (EEG) recordings accurately. The main goal of our work is to facilitate an automated seizure detection system using machine learning algorithms. We proposed a model that evaluates eight machine learning algorithms on the Bonn University dataset. Two classifiers, Random Forest and Gaussian Naive Bayes, achieve the highest accuracy of 100% with 100% sensitivity, 100% specificity, 0.01 FPR, and 0.99 AUC with feature extraction. These two algorithms also work better without using feature extraction. This performance is superior to existing seizure detection approaches and comparable to deep learning approaches. Our work comprehensively compares the traditional machine learning algorithms and reinforces the effectiveness of feature extraction. Our work contributes to aiding neurologists in making faster and more precise decisions for epilepsy treatment.