A Novel Multi-Class EEG-Based Sleep Stage Classification System
A Novel Multi-Class EEG-Based Sleep Stage Classification System
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DOI：
10.1109/tnsre.2017.2776149
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发表时间：
2018-01-01
影响因子：
4.9
通讯作者：
Faradji, Farhad
中科院分区：
文献类型：
--
作者：
Memar, Pejman;Faradji, Farhad
睡眠阶段分类是有效诊断和治疗睡眠相关疾病的最关键步骤之一。睡眠专家进行的目视检查是一项耗时且繁重的任务。因此，计算机辅助睡眠阶段分类系统对于睡眠相关疾病的诊断和睡眠监测至关重要。在本文中，我们提出了一种以高灵敏度和特异性对唤醒和睡眠阶段进行分类的系统。使用来自三个数据集的 25 名疑似睡眠呼吸障碍受试者的脑电图信号和 20 名健康受试者的脑电图信号。每个 EEG 历元被分解为八个子带历元，每个子带历元都有一个属于一种 EEG 节律的频带（即 delta、theta、alpha、sigma、beta 1、beta 2、gamma 1 或 gamma 2）。从每个子带历元中提取了十三个特征。因此，每个 EEG 历元总共获得 104 个特征。 Kruskal-Wallis 检验用于检查特征的重要性。不重要的特征被丢弃。然后使用最小冗余最大相关特征选择算法来消除冗余和不相关的特征。所选特征由随机森林分类器进行分类。为了设置系统参数并评估系统性能，执行嵌套5折交叉验证和主题交叉验证。我们提出的系统的性能针对不同的多类分类问题进行了评估。嵌套 5 重和受试者交叉验证的最低总体准确率分别为 95.31% 和 86.64%。与最先进的系统相比，该系统在准确性、灵敏度和特异性方面的性能很有希望。所提出的系统可用于医疗保健应用，旨在改善睡眠阶段分类。
Sleep stage classification is one of the most critical steps in effective diagnosis and the treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time-consuming and burdensome task. A computer-assisted sleep stage classification system is thus essential for both sleep-related disorders diagnosis and sleep monitoring. In this paper, we propose a system to classify the wake and sleep stages with high rates of sensitivity and specificity. The EEG signals of 25 subjects with suspected sleep-disordered breathing, and the EEG signals of 20 healthy subjects from three data sets are used. Every EEG epoch is decomposed into eight subband epochs each of which has a frequency band pertaining to one EEG rhythm (i.e., delta, theta, alpha, sigma, beta 1, beta 2, gamma 1, or gamma 2). Thirteen features are extracted from each subband epoch. Therefore, 104 features are totally obtained for every EEG epoch. The Kruskal-Wallis test is used to examine the significance of the features. Non-significant features are discarded. The minimal-redundancy-maximal-relevance feature selection algorithm is then used to eliminate redundant and irrelevant features. The features selected are classified by a random forest classifier. To set the system parameters and to evaluate the system performance, nested 5-fold cross-validation and subject cross-validation are performed. The performance of our proposed system is evaluated for different multi-class classification problems. The minimum overall accuracy rates obtained are 95.31% and 86.64% for nested 5-fold and subject cross-validation, respectively. The system performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art systems. The proposed system can be used in health care applications with the aim of improving sleep stage classification.