Temko Andriy, Lightbody Gordon, Boylan Geraldine, Marnane William
Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1447-50. doi: 10.1109/IEMBS.2011.6090358.
A framework for online dynamic channel weighting is developed for the task of EEG-based neonatal seizure detection. The channel weights are computed on-the-fly by combining the up-to-now patient-specific history and the clinically-derived prior channel importance. These estimated time-varying weights are introduced within a Bayesian probabilistic framework to provide a channel-specific and thus patient-adaptive seizure classification scheme. Validation results on one of the largest clinical datasets of neonatal seizures confirm the utility of the proposed channel weighting for the SVM-based detector recently developed by this research group. Exploiting the channel weighting, the precision-recall area can be drastically increased (up to 25%) for the most difficult patients, with the average increase from 81.0% to 84.42%. It is also shown that the increase in performance with channel weighting is proportional to the time the patient is observed.
针对基于脑电图的新生儿癫痫检测任务，开发了一种在线动态通道加权框架。通过结合到目前为止特定患者的病史和临床得出的先前通道重要性，实时计算通道权重。这些估计的时变权重被引入贝叶斯概率框架内，以提供一种通道特定且因此针对患者自适应的癫痫分类方案。在最大的新生儿癫痫临床数据集之一上的验证结果证实了所提出的通道加权对于该研究小组最近开发的基于支持向量机的检测器的实用性。利用通道加权，对于最难检测的患者，精确召回率面积可大幅增加（高达25%），平均从81.0%提高到84.42%。研究还表明，通道加权带来的性能提升与观察患者的时间成正比。
