Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:398-401. doi: 10.1109/EMBC48229.2022.9871043.
Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS-induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS-evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from healthy controls was investigated. A dataset of TMS-EEG recordings from 17 AD and 17 healthy controls (HC) was used in our analysis. A Random Forest classification algorithm was trained on the extracted TEP metrics and its performance is evaluated in a leave-one-subject-out cross-validation. The created model showed promising results in identifying AD patients from HC with an accuracy, sensitivity and specificity of 69.32%, 72.23% and 66.41%, respectively. Clinical relevance- Three preliminary metrics were proposed to quantify the strength and duration of the response to TMS on EEG data. The proposed metrics were successfully used to identify Alzheimer's disease patients from healthy controls. These results proved the potential of this approach which will provide additional diagnostic value.
经颅磁刺激（TMS）结合脑电图记录（TMS-EEG）在研究大脑，特别是阿尔茨海默病（AD）方面显示出巨大的潜力。在这项研究中，我们提出了一种自动确定 TMS 诱导的脑电图信号干扰持续时间的方法，作为反映大脑功能改变的潜在指标。在阿尔茨海默病（AD）患者中进行了初步研究。提出了三种用于描述 TMS 诱发脑电图（TEP）活动强度和持续时间的指标，并研究了它们在从健康对照者中识别 AD 患者的潜力。我们的分析使用了来自 17 名 AD 患者和 17 名健康对照者（HC）的 TMS-EEG 记录数据集。在提取的 TEP 指标上训练了随机森林分类算法，并在留一受试者交叉验证中评估其性能。所创建的模型在从 HC 中识别 AD 患者方面显示出有希望的结果，其准确性、敏感性和特异性分别为 69.32%、72.23%和 66.41%。临床相关性-提出了三个初步指标来量化 TMS 对脑电图数据的反应强度和持续时间。所提出的指标成功地用于从健康对照者中识别阿尔茨海默病患者。这些结果证明了这种方法的潜力，它将提供额外的诊断价值。
