Temko Andriy, Thomas Eoin, Boylan Geraldine, Marnane William, Lightbody Gordon
Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2643-6. doi: 10.1109/IEMBS.2009.5332807.
This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and robustness of the system and their influence on performance is shown. The SVM-based system is evaluated on a large clinical dataset using several epoch-based and event based metrics and curves of performance are reported. Additionally, a new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics.
这项工作提出了一种基于支持向量机（SVM）分类器的多通道独立于患者的新生儿癫痫检测系统。提出了几个后处理步骤以提高系统的时间精度和鲁棒性，并展示了它们对性能的影响。基于支持向量机的系统在一个大型临床数据集上进行评估，使用了几个基于时段和基于事件的指标，并报告了性能曲线。此外，还提出了一种测量误检平均持续时间的新指标，以配合基于事件的指标。
