Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland.
IEEE Trans Biomed Eng. 2012 Mar;59(3):717-27. doi: 10.1109/TBME.2011.2178411. Epub 2011 Dec 7.
A measure of bipolar channel importance is proposed for EEG-based detection of neonatal seizures. The channel weights are computed based on the integrated synchrony of classifier probabilistic outputs for the channels which share a common electrode. These estimated time-varying weights are introduced within a Bayesian probabilistic framework to provide a channel specific and, thus, adaptive seizure classification scheme. Validation results on a clinical dataset of neonatal seizures confirm the utility of the proposed channel weighting for the two patient-independent seizure detectors recently developed by this research group: one based on support vector machines (SVMs) and the other on Gaussian mixture models (GMMs). By exploiting the channel weighting, the receiver operating characteristic (ROC) area can be significantly increased for the most difficult patients, with the average ROC area across 17 patients increased by 22% (relative) for the SVM and by 15% (relative) for the GMM-based detector, respectively. It is shown that the system developed here outperforms the recent published studies in this area.
提出了一种用于基于脑电图的新生儿癫痫检测的双极通道重要性度量。通道权重是根据共享共同电极的通道的分类器概率输出的综合同步性计算的。这些估计的时变权重被引入到贝叶斯概率框架中，以提供一种特定于通道的、因此是自适应的癫痫分类方案。对本研究小组最近开发的两种患者独立癫痫检测器的临床数据集的验证结果证实了所提出的通道加权的有效性：一种基于支持向量机 (SVM)，另一种基于高斯混合模型 (GMM)。通过利用通道加权，可以显著提高最困难患者的接收者操作特性 (ROC) 面积，对于 SVM 和基于 GMM 的检测器，17 名患者的平均 ROC 面积分别增加了 22%（相对）和 15%（相对）。结果表明，这里开发的系统在该领域优于最近发表的研究。
