论文标题
关于脑电图密度对TMS-EEG分类对阿尔茨海默氏病的影响的初步研究
Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease
论文作者
论文摘要
与脑电图（TMS-EEG）共同注册的经颅磁刺激先前已证明是对阿尔茨海默氏病（AD）研究的有用工具。在这项工作中，我们研究了使用TMS诱发的脑电图反应对健康对照（HC）分类的AD患者的使用。通过使用包含17AD和17HC的数据集，我们从单个TMS响应中提取各种时域特征，并在低，中和高密度的EEG电极集中平均它们。在一项外出的验证方案中，使用带有随机森林分类器的高密度电极获得了AD与HC的最佳分类性能。准确性，灵敏度和特异性分别为92.7％，96.58％和88.2％。
Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.2% respectively.
