Sharmila A
a School of Electrical Engineering , VIT , Vellore , India.
J Med Eng Technol. 2018 Jul;42(5):368-380. doi: 10.1080/03091902.2018.1513576. Epub 2018 Nov 22.
Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.
几十年来,人们一直在尝试进行癫痫发作检测的研究,以支持自动诊断系统,帮助临床医生减轻繁重的工作负担。在这方面,已经发表了大量关于癫痫发作识别的研究论文。很难对所有这些文献进行详细综述。因此,本文尝试对癫痫发作检测进行综述。已经讨论了100多篇研究论文,以识别癫痫发作的检测技术。此外,文献调查表明,检测癫痫发作所需的模式识别因脑电图数据集的不同条件而异。这主要是因为在不同条件下检测到的脑电图具有不同的特征。反过来,这就需要识别模式识别技术,以便有效地从各种脑电图数据条件中区分出癫痫脑电图数据。