Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Shikatacho 2-chome 5-1, Kita-ku, Okayama 700-8558, Japan.
Epilepsy Res. 2011 Oct;96(3):276-82. doi: 10.1016/j.eplepsyres.2011.06.012. Epub 2011 Jul 28.
To improve the interpretability of figures containing an amplitude-integrated electroencephalogram (aEEG), we devised a color scale that allows us to incorporate spectral edge frequency (SEF) information into aEEG figures. Preliminary clinical assessment of this novel technique, which we call aEEG/SEF, was performed using neonatal and early infantile seizure data.
We created aEEG, color density spectral array (DSA), and aEEG/SEF figures for focal seizures recorded in seven infants. Each seizure was paired with an interictal period from the same patient. After receiving instructions on how to interpret the figures, eight test reviewers examined each of the 72 figures displaying compressed data in aEEG, DSA, or aEEG/SEF form (12 seizures and 12 corresponding interictal periods) and attempted to identify each as a seizure or otherwise. They were not provided with any information regarding the original record.
The median number of correctly identified seizures, out of a total of 12, was 7 (58.3%) for aEEG figures, 8 (66.7%) for DSA figures and 10 (83.3%) for aEEG/SEF figures; the differences among these are statistically significant (p=0.011). All reviewers concluded that aEEG/SEF figures were the easiest to interpret.
The aEEG/SEF data presentation technique is a valid option in aEEG recordings of seizures.
为提高包含振幅整合脑电图（aEEG）的图像的可解释性，我们设计了一种颜色标度，可以将频谱边缘频率（SEF）信息纳入到 aEEG 图像中。我们对这项称为 aEEG/SEF 的新技术进行了初步的临床评估，该技术使用新生儿和婴儿早期的癫痫发作数据。
我们为 7 名婴儿记录的局灶性癫痫发作创建了 aEEG、彩色密度谱图（DSA）和 aEEG/SEF 图。每个癫痫发作都与来自同一患者的发作间期相对应。在收到如何解释这些图的说明后，8 名测试审阅者检查了以 aEEG、DSA 或 aEEG/SEF 形式显示压缩数据的 72 个图中的每一个（12 个癫痫发作和 12 个相应的发作间期），并尝试将其识别为癫痫发作或其他情况。他们没有提供任何有关原始记录的信息。
在总共 12 个癫痫发作中，正确识别的癫痫发作中位数为 7 个（58.3%），aEEG 图为 8 个（66.7%），DSA 图为 10 个（83.3%）；这些差异具有统计学意义（p=0.011）。所有审阅者都认为 aEEG/SEF 图最容易解释。
在癫痫发作的 aEEG 记录中，aEEG/SEF 数据呈现技术是一种有效的选择。
