Temko A, Boylan G, Marnane W, Lightbody G
Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3281-4. doi: 10.1109/IEMBS.2010.5627260.
In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.
在这项工作中，通常用于自动语音识别（ASR）的特征被用于检测新生儿脑电图中的新生儿惊厥。将三种传统的ASR特征集与先前针对此任务开发的特征集进行比较。结果表明，经过深入研究的基于频谱包络的ASR特征自身表现相当不错。此外，支持向量机递归特征消除程序应用于汇总在一起的所有提取特征。结果表明，ASR特征始终出现在顶级特征之中。
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