Bogaarts J G, Gommer E D, Hilkman D M W, van Kranen-Mastenbroek V H J M, Reulen J P H
Department of Clinical Neurophysiology, AZM Maastricht, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands,
Ann Biomed Eng. 2014 Nov;42(11):2360-8. doi: 10.1007/s10439-014-1089-2. Epub 2014 Aug 15.
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.
我们项目的目标是使用支持向量机（SVM）进一步优化新生儿癫痫发作检测。首先，使用卡尔曼滤波器（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，引入脑电图基线特征校正（FBC）以减少患者间特征分布的变异性。在来自39名足月儿和早产儿的54份多通道常规脑电图记录上评估检测方法的性能。使用接收器操作特征曲线（AUC）下的面积以及敏感性和特异性来评估分类方法的性能。未使用KF和FBC的SVM实现的AUC为0.767（敏感性0.679，特异性0.707）。在用于训练数据预处理的卡尔曼平滑器和用于对分类器输出进行滤波的KF的基线校正特征上，实现了最高的AUC为0.902（敏感性0.801，特异性0.831）。FBC和KF都显著改善了新生儿癫痫发作检测。本文介绍了基于SVM的新生儿癫痫发作检测技术的重大改进。
