Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments available for seizure control have improved a lot with the advancements in the field of medical science and technology. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is performed before surgery and also to predict seizure at the time operation which is useful in neuro stimulation device. But in most of cases visual examination is done by neurologist in order to detect and classify patterns of the disease but this requires a lot of pre-domain knowledge and experience. This all in turns put a pressure on neurosurgeons and leads to time wastage and also reduce their accuracy and efficiency. There is a need of some automated systems in arena of information technology like use of neural networks in deep learning which can assist neurologists. In the present paper, a model is proposed to give an accuracy of 98.33% which can be used for development of automated systems. The developed system will significantly help neurologists in their performance.
翻译:癫痫是公元前4000年全球出现的最常见神经疾病之一。它影响着目前所有年龄的大约5 000万人。该疾病的特征是经常发病。在过去几十年中,随着医学科技领域的进步,用于控制缉获的治疗方法有了很大的改进。脑脑图是一种广泛应用的技术,用于监测大脑活动,并广泛流行用于缉获区域检测。它是在手术前进行,还用来预测在操作时的缉获,这对神经刺激装置有用。但在大多数情况下,视觉检查是由神经学家进行,以便检测和分类疾病的模式,但需要大量的预科知识和经验。这反过来又给神经外科医生带来压力,导致时间浪费,还降低了时间的准确性和效率。在信息技术领域需要一些自动化系统,例如利用神经网络进行深层学习,从而帮助神经学家。在本文件中,提出一种模型,可以提供98.33%的精确度,用于发展自动化系统。发达的系统将大大地帮助神经系统的运作。