Machine Learning-based Approach for Stroke Classification using Electroencephalogram (EEG) Signals

In recent years, the health care field has heavily relied on the field of computation. The medical decision support system DSS, for instance, helps health professionals obtain accurate and reliable readings and diagnosis of patients' vital signs. Nowadays, several medical devices allow capturing brain signals, some of these devices are wearable, which enhances signal quality and facilitates access to the signals than the traditional EEG devices. EEG signals are critical for assessing mental health and analyzing brain characteristics as they are able to detect a wide range of nerve-related diseases, such as stroke. This research seeks to study the use of machine learning techniques for the medical diagnosis of stroke through EEG signals obtained from the wearable device 'MUSE 2.' Eight ML techniques were used for analysis, the XGboost classifiers outperformed other classifiers in identifying strokes with an accuracy rate of 83.89%. The findings proved a 7.89% improvement on accuracy from the previous study "Predicting stroke severity with a 3-minute recording from the Muse portable EEG study.