Sakkalis Vangelis, Giannakakis Giorgos, Farmaki Christina, Mousas Abdou, Pediaditis Matthew, Vorgia Pelagia, Tsiknakis Manolis
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:6333-6. doi: 10.1109/EMBC.2013.6611002.
In this study, we investigated three measures capable of detecting absence seizures with increased sensitivity based on different underlying assumptions. Namely, an information-based method known as Approximate Entropy, a nonlinear alternative (Order Index), and a linear variance analysis approach. The results on the long-term EEG data suggest increased accuracy in absence seizure detection achieving sensitivity as high as 97.33% with no further application of any sophisticated classification scheme.
在本研究中，我们基于不同的潜在假设，研究了三种能够以更高灵敏度检测失神发作的方法。具体而言，一种基于信息的方法，称为近似熵；一种非线性替代方法（阶次指数）；以及一种线性方差分析方法。对长期脑电图数据的分析结果表明，在不进一步应用任何复杂分类方案的情况下，失神发作检测的准确性有所提高，灵敏度高达97.33%。
