Stroke Detection Using EEG and Deep Learning: A Comparative Study of Feature Engineering Techniques - May Issa Aldossary; Fatemah H. Alghamedy; Dina A. Alabbad; Reem A. H. Alshami; Haya A. Alzahim; Renad A. Alnuaim; Maimonah S. Altaweel; Shahad F. Alotaibi; Sumayh S. Aljameel; Areej A. Almalki; Sunday O. Olatunji (Ital Publication, 2026)
Strokes remain one of the leading causes of disability and mortality worldwide, underscoring the need for effective early detection and intervention methods. Recently, researchers have shown a growing interest in harnessing bio-signals, natural indicators produced by the human body, as potential markers for stroke detection. Multiple types of bio-signals, such as electroencephalography (EEG), are currently being explored in stroke diagnostic studies. This approach is promising because it offers a non-invasive, cost-effective, accurate, and portable means of detecting strokes. The objectives of this research are to investigate the effectiveness of deep learning (DL) techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), CNN-LSTM hybrid models, and CNN-Gated Recurrent Unit (CNN-GRU) models, for detecting early-stage strokes based on EEG data. In addition, the impact of diverse feature extraction techniques, including utilizing all features, selecting features with a Decision Tree (DT) based on different thresholds, Principal Component Analysis (PCA), and Independent Component Analysis (ICA), is analyzed to evaluate their influence on model performance. A comparative discussion is conducted across multiple experimental setups to identify the most effective DL and feature engineering combinations for stroke detection. Across 35 different experiments, the CNN-LSTM model with seven selected features using the DT method yields the best results, achieving 86% accuracy, 99% precision, 81% recall, and an F1-score of 89%.