Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting
Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting
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DOI：
10.1016/j.cmpb.2016.12.015
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发表时间：
2017-03-01
影响因子：
6.1
通讯作者：
Bhuiyan, Mohammed Imamul Hassan
中科院分区：
文献类型：
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作者：
Hassan, Ahnaf Rashik;Bhuiyan, Mohammed Imamul Hassan
背景和目的：自动睡眠分期对于减轻医生通过目视检查分析大量数据的负担至关重要。这也是使自动睡眠监测系统可行的先决条件。此外，计算机化的睡眠评分将加快睡眠研究中的大规模数据分析。然而，大多数关于睡眠分期的现有工作是基于多通道或多个生理信号的，这对于用户来说是不舒服的，并且阻碍了家庭睡眠监测设备的可行性。因此，一个成功的和可靠的计算机辅助睡眠stagingscheme尚未出现。方法：在这项工作中，我们提出了一个单通道EEG为基础的计算机睡眠评分算法。在该算法中，我们分解的EEG信号段使用Ensemble经验模式分解（EEMD）和提取各种统计矩为基础的功能。EEMD和统计特征的有效性进行了研究。统计分析进行特征选择。介绍了一种新提出的分类技术，即随机采样下提升（RUSBoost）的睡眠阶段分类。据作者所知，这是EEMD与RUSBoost结合的第一次实施。所提出的特征提取方案的性能进行了研究的各种选择的分类模型。我们的计划的算法性能进行评估，对当代作品在literature.Results：所提出的方法的性能是可比的或优于国家的最先进的。该算法对Sleep-EDF数据库的睡眠阶段的6-状态到2-状态分类给出了88.07%，83.49%，92.66%，94.23%和98.15%。我们的实验结果表明，RUSBoost优于其他分类模型的特征提取框架中提出的这项工作。此外，在这项工作中提出的算法表现出较高的检测精度的睡眠状态S1和REM。结论：统计矩为基础的特征在EEMD域区分睡眠状态成功和有效的。本文提出的自动化睡眠评分方案可以消除临床医生的负担，有助于睡眠监测系统的设备实现，并且有益于睡眠研究。(C)2016爱思唯尔爱尔兰有限公司版权所有。
Background and objective: Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge.Methods: In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature.Results: The performance of the proposed method is comparable or better than that of the state-of-theart ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM.Conclusion: Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. (C) 2016 Elsevier Ireland Ltd. All rights reserved.