Greene B R, Boylan G B, Marnane W P, Lightbody G, Connolly S
Department of Electrical & Electronic Engineering, University College Cork, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:915-8. doi: 10.1109/IEMBS.2008.4649303.
Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with poor long-term outcome. EEG is considered the gold standard for identification of all neonatal seizures, reducing the number of EEG electrodes required would reduce patient handling and allow faster acquisition of data. A method for automated neonatal seizure detection based on two carefully chosen cerebral scalp electrodes but trained using multi-channel EEG is presented. The algorithm was developed and tested using a multi-channel EEG dataset containing 411 seizures from 251.9 hours of EEG recorded from 17 full-term neonates. Automated seizure detection using a variety of bipolar channel derivations was investigated. Channel C3-C4 yielded correct detection of 90.77% of seizures with a false detection rate of 9.43%. This compares favourably with a multi-channel seizure detection method which detected 81.03% of seizures with a false detection rate of 3.82%.
新生儿惊厥是新生儿期最常见的神经系统急症，且与远期不良预后相关。脑电图（EEG）被认为是识别所有新生儿惊厥的金标准，减少所需的EEG电极数量将减少对患者的操作，并能更快地获取数据。本文提出了一种基于两个精心挑选的头皮脑电电极、但使用多通道EEG进行训练的新生儿惊厥自动检测方法。该算法是使用一个多通道EEG数据集开发和测试的，该数据集包含从17名足月儿记录的251.9小时EEG中的411次惊厥。研究了使用各种双极通道推导进行惊厥自动检测的情况。通道C3-C4对90.77%的惊厥检测正确，假阳性率为9.43%。这与一种多通道惊厥检测方法相比具有优势，后者检测出81.03%的惊厥，假阳性率为3.82%。
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