MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-06, Vol.24 (13), p.4234
• Main Authors: Tong, Wei, Yue, Weiqi, Chen, Fangni, Shi, Wei, Zhang, Lei, Wan, Jian
• Format: Article
• Language: English
• Subjects: Accuracy; Aged; Algorithms; Automatic classification; Brain Ischemia - diagnosis; Brain Ischemia - physiopathology; Brain research; Datasets; Deep Learning; Disease; Electroencephalography; Electroencephalography - methods; Emergency medical care; Entropy; Epilepsy; feature fusion; Female; Humans; Ischemia; ischemic stroke; Ischemic Stroke - diagnosis; Ischemic Stroke - physiopathology; Magnetic resonance imaging; Male; Methods; Middle Aged; Neural networks; Signal Processing, Computer-Assisted; Stroke; Stroke - diagnosis; Stroke - physiopathology
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24134234

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.