论文标题
使用Pearson的产品矩相关系数的癫痫发作预测，从广义高斯建模中的线性分类器
Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling
论文作者
论文摘要
为了预测癫痫事件意味着能够提前确定癫痫发作时间具有最高的精度。临床应用中癫痫事件的正确预测基准是生物医学信号处理中的一个典型问题，有助于对该疾病进行适当的诊断和治疗。在这项工作中，我们使用Pearson的产品矩相关系数，从癫痫eeg信号中的癫痫发作和非塞氏菌事件之间进行了通用的高斯分布参数，并预测癫痫发作和非西部事件之间。来自9例患者的36例癫痫事件的表现表现出良好的表现，其敏感性和特异性有效性的100％大于83％的癫痫发作事件。皮尔逊（Pearson）的测试表明，所有脑节奏在非赛式事件中都高度相关，但在癫痫发作期间没有。这表明我们的模型可以用Pearson的产品矩相关系数来缩放，以检测癫痫发作。
To predict an epileptic event means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications is a typical problem in biomedical signal processing that helps to an appropriate diagnosis and treatment of this disease. In this work, we use Pearson's product-moment correlation coefficient from generalized Gaussian distribution parameters coupled with a linear-based classifier to predict between seizure and non-seizure events in epileptic EEG signals. The performance in 36 epileptic events from 9 patients showing good performance with 100% of effectiveness for sensitivity and specificity greater than 83% for seizures events in all brain rhythms. Pearson's test suggests that all brain rhythms are highly correlated in non-seizure events but no during the seizure events. This suggests that our model can be scaled with the Pearson's product-moment correlation coefficient for the detection of epileptic seizures.
