论文标题
使用信号能量表征TMS-EEG扰动指数：关于阿尔茨海默氏病分类的初步研究
Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification
论文作者
论文摘要
结合脑电图记录（TMS-EEG）的经颅磁刺激（TMS）在大脑，尤其是阿尔茨海默氏病（AD）的研究中表现出巨大的潜力。在这项研究中，我们提出了一种自动方法，用于确定TMS诱导的脑电图信号的持续时间，作为反映大脑功能改变的潜在度量。对阿尔茨海默氏病（AD）患者进行了初步研究。提出了三个用于表征TMS引起的脑电图（TEP）活性的强度和持续时间的指标，并研究了它们从健康对照中识别AD患者的潜力。在我们的分析中，使用了来自17个AD和17个健康对照（HC）的TMS-EEG录音数据集。对提取的TEP指标进行了随机森林分类算法的培训，并在一项受试者的交叉验证中评估其性能。创建的模型在鉴定来自HC的AD患者的结果中表现出令人鼓舞的结果，其精度，灵敏度和特异性分别为69.32％，72.23％和66.41％。
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.
