Nagaraj S B, Stevenson N J, Marnane W P, Boylan G B, Lightbody G
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2829-32. doi: 10.1109/EMBC.2014.6944212.
In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93.
在本文中，我们检验了基于时频分布（TFD）的特征集用于新生儿脑电图癫痫检测的稳健性。该特征集最初是在文献中提出的，用于使用支持向量机（SVM）进行新生儿癫痫检测。我们以平滑维格纳-威利分布和修正B分布作为基础时频分布，测试了该特征集的性能。使用修正B分布从脑电图信号的时频分布中提取时频信号和图像处理特征的癫痫检测系统，在来自18名足月新生儿的826小时脑电图数据的大型临床数据集上进行测试，该数据集包含1389次癫痫发作，其能够实现中位数接收者操作特征面积为0.96（四分位距0.91 - 0.98）。平均AUC为0.93。
