在分析中早期发现和分类癫痫发作的好处,对计算机辅助设备和最近的医疗物联网(IoMT)设备的实现和实施进行监控和诊断,这一点怎么强调都不为过。这些应用的成功很大程度上取决于所采用的检测和分类技术的准确性。已经研究了几种方法,多年来提出和发展。本文研究了近十年来各种癫痫发作检测算法和分类,包括传统技术和最近的深度学习算法。它还讨论了癫痫样检测作为对意识障碍(DOC)的高级诊断及其理解的步骤之一。对所研究的不同算法进行了性能比较,并探讨了它们的优缺点。从我们的调查来看,最近,人们非常关注探索深度学习算法在癫痫发作检测和分类中的功效,用于其他领域,如图像处理和分类。混合深度学习也得到了探索,CNN-RNN是最受欢迎的。
The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.