MACHINE LEARNING-BASED STROKE DISEASE DIAGNOSIS USING ELECTROENCEPHALOGRAM (EEG) SIGNALS

Abstract

Stroke is currently ranked as the third leading cause of death worldwide. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they have their limitations. CT scans can be time-consuming, taking up to 8 hours to complete diagnosis, while MRI procedures can be lengthy, often making it impractical for most stroke patients. This has led to the necessity of exploring new methods for stroke detection, particularly utilizing EEG signals. In this paper, we propose a cloud computingbased machine learning (ML) system that leverages MUSE2 to diagnose stroke

patients by analysing EEG signals. Our dataset, collected from Al Bashir Hospital

between

2021 and 2022, consists of a randomly selected sample of 31 stroke patients and 31 healthy individuals. To pre-process the collected dataset, we employ Fourier and wavelet transformations. The processed EEG signals are then transmitted over the Internet to the ML model for stroke diagnosis. Real-time results are delivered to authorized personnel via SMS. During our research, various classifiers were evaluated, and a modified XGboost classifier emerged as the most effective choice. It outperformed other ML classifiers with an impressive accuracy of 96.87%.