Cacciotti Alessia, Pappalettera Chiara, Miraglia Francesca, Carrarini Claudia, Pecchioli Cristiano, Rossini Paolo Maria, Vecchio Fabrizio
Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy.
Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
Geroscience. 2025 Feb;47(1):977-992. doi: 10.1007/s11357-024-01301-1. Epub 2024 Aug 1.
Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.
中风是一种严重的病症，可能导致永久性残疾状况。中风后的最初8周对康复至关重要，因为大多数恢复都发生在这一时期。需要个性化方法和预测性生物标志物来进行针对性康复。在此背景下，脑电图大脑连接性和人工智能（AI）在有效诊断和预测中风结果方面可发挥关键作用。在本研究中，纳入了127例亚急性缺血性病变患者和90例年龄及性别匹配的健康对照者。在中风发作后15天内从每位参与者获取脑电图记录。使用美国国立卫生研究院卒中量表（NIHSS）在基线和40天随访时进行临床评估。使用总相干性（TotCoh）和小世界（SW）进行功能连接性分析。实施二次支持向量机（SVM）算法来对健康受试者与中风患者进行分类（健康与中风）、确定受影响的半球（左半球与右半球）以及预测功能恢复（功能恢复预测）。在功能恢复预测分类中，准确率达到94.75%，灵敏度为96.27%，特异性为92.33%，曲线下面积（AUC）为0.95；对于健康与中风分类，准确率为99.09%，灵敏度为100%，特异性为98.46%，AUC为0.99。对于左半球与右半球分类，准确率为86.77%，灵敏度为91.44%，特异性为80.33%，AUC为0.87。这些发现凸显了结合基于脑电图的功能连接性测量与人工智能算法以通过针对性康复干预改善患者预后的潜力。
