深度学习在癫痫中的应用

G. Sharma
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引用次数: 0

摘要

在过去的几十年里,慢性疾病一直在持续上升,其中癫痫是最常见的神经系统疾病。然而,由于医学科学最近取得的进展,大约70%的癫痫病例可以得到控制。对于癫痫的诊断,脑电图、CT扫描、MRI等是最常见的几种方法,但在本章中,将重点介绍脑电图诊断。虽然脑电图可以被认为是一种确定癫痫结果的好方法,证明一个人是否患有癫痫,但它不是一种完全可靠的方法。因此,为了准确检测它,我们必须使用复杂的技术,如CNN和LSTM,将提供及时和正确的诊断,减少频繁癫痫发作和SUDEP的机会。使用抗癫痫药物并不能保证预防癫痫,即使可以,这些药物也会带来一些严重的副作用,所以人们必须回到瑜伽中寻求可能的永久治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Deep Learning in Epilepsy
Over the past few decades, chronic illnesses have been on a continuous rise of which epilepsy has been the most common neurological disorder. However, due to the recent progress that has been made by medical science, epilepsy can be controlled for about 70% of the cases. To diagnose epilepsy, EEG, CT scan, MRI, etc. are some of the most common ways, but in this chapter, diagnosis using EEG shall be most focused upon. Although EEG can be considered a good way to decide upon the results of epilepsy proving whether a person is epileptic or not, it is not a completely reliable method. Hence, for its accurate detection we must use sophisticated techniques like CNN and LSTM that will provide a timely and correct diagnosis, reducing the chances of frequent epileptic seizures and SUDEP. Using anti-epileptic drugs cannot guarantee epilepsy prevention, and even if they do, these drugs come with some serious side effects, so people must look back to yoga for a probable permanent treatment.
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