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Abstract：
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.
摘要：
缺血性卒中是由脑血管的病理变化引起的一种脑功能障碍，导致脑组织缺血缺氧，最终导致细胞坏死。在早期时间窗内没有及时有效的治疗，缺血性卒中可导致长期残疾甚至死亡。因此,快速检测对缺血性卒中患者至关重要。在这项研究中,我们开发了一种基于从脑电图(EEG)信号中提取的融合特征的深度学习模型,用于快速检测缺血性卒中.具体来说,我们招募了20例缺血性卒中患者,这些患者在卒中急性期接受了EEG检查,并收集了19例无卒中病史的成人的EEG信号作为对照组.之后,我们构建了相关加权相位滞后指数(cwPLI)，一个新颖的特征，探索脑电通道之间的同步信息和功能连通性。此外，通过将cwPLI矩阵和样本熵（SaEn）组合在一起，将来自功能连通性的时空信息和来自复杂性的非线性信息融合在一起，以进一步提高模型的判别能力。最后,采用新型MSE-VGG网络作为分类器来区分缺血性卒中和非缺血性卒中数据.五次交叉验证实验表明，该模型具有优异的性能，准确地说，灵敏度,特异性达到90.17%，89.86%，和90.44%，分别。时间消耗实验验证了所提出的方法优于其他最先进的考试。本研究有助于推进缺血性卒中的快速检测,揭示脑电图未开发的潜力，并证明深度学习在缺血性卒中识别中的功效。
