论文标题
稀疏的动力学生成，应用于帕金森氏病诊断
Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis
论文作者
论文摘要
在这项研究中，我们关注基于脑电图（EEG）信号的帕金森氏病（PD）的诊断。我们提出了一种受大脑功能启发的新方法，该方法使用脑电图的动力学，频率和时间含量来提取疾病的新特征。该方法是在包含在3台球听觉任务中记录的eeg信号的公开数据集上评估的，其中涉及n = 50个受试者，其中25个受试者患有PD。通过提取两个功能，并使用线性判别分析（LDA）分类器将它们分开，我们可以使用单个通道将健康的受试者与不健康受试者分开，精度为90％$（p <0.03）$。通过从三个渠道汇总信息并进行投票，我们获得了94％的准确性，敏感性为96％，特异性为92％。使用嵌套的一对交叉验证程序进行评估，从而防止数据泄漏问题并进行较小的评估。进行了几项测试以评估我们方法的有效性和鲁棒性，包括我们仅使用一半可用数据进行培训的测试。在此约束下，该模型的准确度为83.8％。
In this study we focus on the diagnosis of Parkinson's Disease (PD) based on electroencephalogram (EEG) signals. We propose a new approach inspired by the functioning of the brain that uses the dynamics, frequency and temporal content of EEGs to extract new demarcating features of the disease. The method was evaluated on a publicly available dataset containing EEG signals recorded during a 3-oddball auditory task involving N = 50 subjects, of whom 25 suffer from PD. By extracting two features, and separating them with a straight line using a Linear Discriminant Analysis (LDA) classifier, we can separate the healthy from the unhealthy subjects with an accuracy of 90 % $(p < 0.03)$ using a single channel. By aggregating the information from three channels and making them vote, we obtain an accuracy of 94 %, a sensitivity of 96 % and a specificity of 92 %. The evaluation was carried out using a nested Leave-One-Out cross-validation procedure, thus preventing data leakage problems and giving a less biased evaluation. Several tests were carried out to assess the validity and robustness of our approach, including the test where we use only half the available data for training. Under this constraint, the model achieves an accuracy of 83.8 %.
