Najdi Shirin, Gharbali Ali Abdollahi, Fonseca José Manuel
Computational Intelligence Group of CTS/UNINOVA, Caparica, Portugal.
Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Campus da Caparica, Quinta da Torre, 2829-516, Caparica, Portugal.
Biomed Eng Online. 2017 Aug 18;16(Suppl 1):78. doi: 10.1186/s12938-017-0358-3.
Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process.
In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity.
Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy.
The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.
如今，睡眠质量是健康生活的最重要指标之一，尤其是考虑到大量与睡眠相关的障碍。使用多导睡眠图（PSG）信号识别睡眠阶段是评估睡眠质量的传统方法。然而，睡眠阶段分类的人工过程既耗时、主观又昂贵。因此，为了提高睡眠阶段分类的准确性和效率，研究人员一直在尝试开发自动分类算法。自动睡眠阶段分类主要包括三个步骤：预处理、特征提取和分类。由于分类精度深受所提取特征的影响，一个较差的特征向量会对分类器产生不利影响，并最终导致低分类精度。因此，应特别关注特征提取和选择过程。
本文比较了七种特征选择方法以及两种特征排名聚合方法的性能。使用了22名健康男性和女性的Pz - Oz脑电图、水平眼电图和颏下肌电图记录。从这些记录中提取了一个包含49个特征的综合特征集。所提取的特征是睡眠阶段分类中从时间、频谱、基于熵和非线性类别中使用的最常见且有效的特征。使用三个标准对特征选择方法进行了评估和比较：分类精度、稳定性和相似性。
模拟结果表明，MRMR - MID实现了最高的分类性能，而Fisher方法提供了最稳定的排名。在我们的模拟中，聚合方法的性能处于平均水平，尽管它们已知能产生更稳定的结果和更高的精度。
Borda和RRA排名聚合方法未能显著优于传统的特征排名方法。在传统方法中，其中一些方法的表现略优于其他方法，尽管合适技术的选择取决于用户的计算复杂度和精度要求。
