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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG
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Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG

机译：突发间检测方法在新生脑梗死中分级缺氧缺血性脑病的抗突发检测方法

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Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms, known as inter-bursts, are related to different grades of injury. This study assesses the suitability of an existing inter-burst detection method, developed from preterm infants born <30 weeks of gestational age, to detect inter-bursts in term infants. Different features from the temporal organisation of the inter-bursts are combined using a multi-layer perceptron (MLP) machine learning algorithm to classify four grades of injury in the EEG. We find that the best performing feature, percentage of inter-bursts, has an accuracy of 59.3%. Combining this with the maximum duration of inter-bursts in the MLP produces a testing accuracy of 77.8%, with similar performance to existing multi-feature methods. These results validate the use of the preterm detection method in term EEG and show how simple measures of the inter-burst interval can be used to classify different grades of injury.
机译：脑电图（EEG）是由于出生时缺乏氧气或血液引起的血液或血液引起的重要临床工具。低压波形的特性，称为突发间，与不同的损伤等级有关。本研究评估了现有的突发间检测方法的适用性，从早产儿出生的<30周的孕龄，以检测术语婴儿的间歇性。使用多层的Perceptron（MLP）机器学习算法组合来自突发间脉冲间的时间组织的不同特征，以对脑电图分类四个伤害等级。我们发现最好的表演功能，突发间的百分比，精度为59.3％。将其与MLP中突发间的最大持续时间相结合，产生77.8％的测试精度，具有与现有的多特征方法相似的性能。这些结果验证了术语EEG中的早产检测方法的使用，并显示了突发间间隔的简单测量如何用于对不同的伤害等级进行分类。

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