睡眠期新生儿临床脑电伪影的联合检测技术
The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns
摘要：
本文介绍了一种结合多项式神经网络和决策树技术的新方法，以便从睡眠新生儿的临床脑电图（EEG）中提取可理解的分类规则。这些EEG被心脏、眼球运动、肌肉和噪声伪影严重破坏，因此一些EEG特征与分类问题无关。结合多项式网络和决策树技术，我们发现可理解的分类规则，同时也试图保持他们的分类错误。这种技术被证明优于许多常用的机器学习技术，应用于自动识别睡眠EEG中的伪影。
英文摘要：
In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle and noise artifacts and as a consequence some EEG features are irrelevant to classification problems. Combining the polynomial network and decision tree techniques, we discover comprehensible classification rules whilst also attempting to keep their classification error down. This technique is shown to outperform a number of commonly used machine learning technique applied to automatically recognize artifacts in the sleep EEGs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
