An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model
An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model
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DOI：
10.1016/j.jneumeth.2019.108320
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发表时间：
2019-08-01
影响因子：
3
通讯作者：
Aarabi, Ardalan
中科院分区：
文献类型：
--
作者：
Ghimatgar, Hojat;Kazemi, Kamran;Aarabi, Ardalan
目的：睡眠阶段评分是诊断睡眠障碍的基础。睡眠阶段的视觉评分非常耗时并且容易出现人为错误。在这项工作中，我们介绍了一种有效的方法，以提高睡眠阶段评分和分类的准确性睡眠analysis.Method：在这种方法中，一组最佳的功能，首先从一个池中提取的特征从睡眠EEG epoch使用的特征选择方法的基础上的相关性和冗余分析。然后使用随机森林分类器对EEG片段进行分类。最后，隐马尔可夫模型（HMM）被用来减少假阳性，结合睡眠阶段之间的过渡的时间结构的知识。我们评估了所提出的方法，使用单通道EEG信号从四个公共睡眠EEG数据集根据R&K和AASM指南评分。我们比较了我们的方法与现有的方法使用不同的交叉验证strategies.Results的性能：使用留一法验证策略，我们的方法取得了整体的准确率在（79.4-87.4%）和（77.6-80.4%）的范围内与Kappa值在0.7-0.85的范围内为六阶段（R&K）和五阶段（AASM）分类，分别为。与主题交叉验证方法相比，使用交叉数据集验证策略，我们的方法显示出总体准确率降低了8%。与现有方法的比较：我们的方法在所有多阶段分类中优于现有方法。结论：建议的单一-通道方法可以用于稳健且可靠的睡眠阶段评分，其具有高精度和相对低的复杂度，所述复杂度需要真实的时间应用.
Objective: Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis.Method: In this approach, a set of optimal features was first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies.Results: Using a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4-87.4%) and (77.6-80.4%) with Kappa values in the range of 0.7-0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method.Comparison with existing method(s): Our method outperformed the existing methods for all multi-stage classification.Conclusions: The proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.