Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:394-397. doi: 10.1109/EMBC48229.2022.9870920.
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 88.82% respectively. Clinical relevance- TMS-EEG responses were successfully used to identify Alzheimer's disease patients from healthy controls.
经颅磁刺激与脑电图（TMS-EEG）的联合应用已被证明是研究阿尔茨海默病（AD）的有用工具。在这项工作中，我们研究了使用 TMS 诱发的 EEG 反应对 AD 患者和健康对照（HC）进行分类的方法。通过使用包含 17 名 AD 患者和 17 名 HC 的数据集，我们从单个 TMS 反应中提取了各种时域特征，并在低、中、高密度 EEG 电极集上对其进行平均。在一次受试者外留一验证方案中，使用随机森林分类器对高密度电极进行分类，AD 与 HC 的分类性能最佳。准确率、敏感度和特异性分别为 92.7%、96.58%和 88.82%。临床相关性-TMS-EEG 反应成功地用于从健康对照中识别出阿尔茨海默病患者。
