EEG Feature Pre-processing for Neonatal Epileptic Seizure Detection
EEG Feature Pre-processing for Neonatal Epileptic Seizure Detection
复制标题
DOI：
10.1007/s10439-014-1089-2
复制
发表时间：
2014-11-01
影响因子：
3.8
通讯作者：
Reulen, J. P. H.
中科院分区：
文献类型：
--
作者：
Bogaarts, J. G.;Gommer, E. D.;Reulen, J. P. H.
我们项目的目的是进一步优化使用支持向量机（SVM）的新生儿癫痫检测。首先，利用卡尔曼滤波器（KF）对特征和分类器输出时间序列进行滤波，以提高时间精度。其次，引入EEG基线特征校正（FBC）来减少特征分布的患者间差异。对39例足月和早产儿的54条多通道常规脑电图记录进行了检测。采用受试者工作特征曲线下面积（AUC）、灵敏度和特异性评价该分类方法的性能。不考虑KF和FBC的SVM AUC为0.767（敏感性0.679，特异性0.707）。使用用于训练数据预处理的卡尔曼平滑和用于过滤分类器输出的KF，在基线校正特征上实现了0.902的最高AUC（灵敏度0.801，特异性0.831）。FBC和KF均能显著提高新生儿癫痫发作的检出率。本文介绍了目前基于支持向量机的新生儿癫痫发作检测的重大改进。
Aim of our project is to further optimize neonatal seizure detection using support vector machine (SVM). First, a Kalman filter (KF) was used to filter both feature and classifier output time series in order to increase temporal precision. Second, EEG baseline feature correction (FBC) was introduced to reduce inter patient variability in feature distributions. The performance of the detection methods is evaluated on 54 multi channel routine EEG recordings from 39 both term and pre-term newborns. The area under the receiver operating characteristics curve (AUC) as well as sensitivity and specificity are used to evaluate the performance of the classification method. SVM without KF and FBC achieves an AUC of 0.767 (sensitivity 0.679, specificity 0.707). The highest AUC of 0.902 (sensitivity 0.801, specificity 0.831) is achieved on baseline corrected features with a Kalman smoother used for training data pre-processing and a KF used to filter the classifier output. Both FBC and KF significantly improve neonatal epileptic seizure detection. This paper introduces significant improvements for the state of the art SVM based neonatal epileptic seizure detection.