A review of epileptic seizure detection using machine learning classifiers.
A review of epileptic seizure detection using machine learning classifiers.
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DOI：
10.1186/s40708-020-00105-1
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发表时间：
2020-05-25
影响因子：
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通讯作者：
Hussain, Nasir
中科院分区：
文献类型：
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作者：
Siddiqui, Mohammad Khubeb;Morales-Menendez, Ruben;Hussain, Nasir
癫痫是一种严重的慢性神经系统疾病，可以通过分析脑神经元产生的脑信号来检测。神经元以一种复杂的方式相互连接，与人体器官沟通并产生信号。这些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。这些信号是复杂的、有噪声的、非线性的、非平稳的，并且产生大量的数据。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EEG数据进行分类，并在不影响性能的情况下沿着揭示相关的可感知模式来检测癫痫发作。因此，各种研究人员已经开发了许多使用机器学习分类器和统计特征进行癫痫发作检测的方法。主要的挑战是选择适当的分类器和功能。本文的目的是提出一个概述的各种各样的这些技术在过去几年的基础上的分类统计特征和机器学习分类-“黑盒子”和“非黑盒子”。本文将对癫痫发作的检测和分类以及未来的研究方向进行详细的介绍。
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.