Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatments are available for epilepsy. These treatments require use of anti-seizure drugs but are not effective in controlling frequency of seizure. There is need of removal of an affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process, using EEG graphs, is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of an EEG signal in form of textual one-dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification of EEG data among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus, developed system will significantly help neurosurgeons in the increase of their performance.
翻译:及时诊断对于挽救癫痫病人的生命非常重要。在过去几年里,对癫痫患者有许多治疗方法。这些治疗方法需要使用抗静静剂,但无法有效控制发病频率。需要利用手术清除受影响的地区。脑电图是一种广泛使用的技术,用于监测脑活动,并广泛流行于抓获地区检测。在手术前用于定位受影响地区。这个人工过程,使用EEEG图,耗时且需要深厚的专业知识。在本文中,提出了一个模型,以文本一维矢量的形式保存EEG信号的真实性质。拟议的模型使波恩大学数据集的艺术性能达到平均灵敏度和特性分别达到81%和81.4%,用于所有五级的EG数据分类。此外,对于二进级分类,达到99.9%,99.5%的特性和灵敏度得分值,而不是其他研究人员使用的2D模型。因此,发达的系统将极大地帮助神经外科外科员增加其性能。