Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

中文翻译：

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使用机器学习分类器检测癫痫发作的综述。

癫痫是一种严重的慢性神经系统疾病，可以通过分析大脑神经元产生的大脑信号来检测。神经元以复杂的方式相互连接，与人体器官进行通信并产生信号。这些大脑信号的监测通常使用脑电图 (EEG) 和皮质电图 (ECoG) 介质来完成。这些信号复杂、有噪声、非线性、非平稳，并且会产生大量数据。因此，癫痫发作的检测和大脑相关知识的发现是一项具有挑战性的任务。机器学习分类器能够对脑电图数据进行分类并检测癫痫发作，并在不影响性能的情况下揭示相关的感知模式。因此，各种研究人员开发了多种使用机器学习分类器和统计特征来检测癫痫发作的方法。主要挑战是选择合适的分类器和特征。本文的目的是基于统计特征和机器学习分类器（“黑盒”和“非黑盒”）的分类，概述过去几年中这些技术的各种变化。所提出的最先进的方法和思想将使人们对癫痫检测和分类以及未来的研究方向有详细的了解。