主成分分析
模式识别(心理学)
共线性
癫痫
人工智能
逻辑回归
脑电图
计算机科学
相关性
特征(语言学)
统计
回归
正规化(语言学)
主成分回归
数学
医学
语言学
哲学
几何学
精神科
作者
Xi Li,Yuanhua Qiao,Lijuan Duan,Jun Miao
标识
DOI:10.1080/10255842.2024.2321991
摘要
Epilepsy is a chronic brain disease caused by excessive discharge of brain neurons. Long-term recurrent seizures bring a lot of trouble to patients and their families. Prediction of different stages of epilepsy is of great significance. We extract pearson correlation coefficients (PCC) between channels in different frequency bands as features of EEG signals for epilepsy stages prediction. However, the features are of large feature dimension and serious multi-collinearity. To eliminate these adverse influence, the combination of traditional dimension reduction method principal component analysis (PCA) and logistic regression method with regularization term is proposed to avoid over-fitting and achieve the feature sparsity. The experiments are conducted on the widely used CHB-MIT dataset using different regularization terms L1 and L2, respectively. The proposed method identifies various stages of epilepsy quickly and efficiently, and it presents the best average accuracy of 94.86%, average precision of 96.71%, average recall of 93.48%, average kappa value of 0.90 and average Matthews correlation coefficient (MCC) value of 0.90 for all patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI