Background

: Automatic sleep stage classification depends crucially on the selection of a good set of descriptive features. However, the selection of a feature set with an appropriate low computational cost without compromising classification performance is still a challenge. This study attempts to represent sleep EEG patterns using a minimum number of features, without significant performance loss.

Methods

: Three feature selection algorithms were applied to a high dimensional feature space comprising 84 features. These methods were based on a bootstrapping approach guided by Gini ranking and mutual information between the features. The algorithms were tested on three scalp electroencephalography (EEG) and one ear-EEG datasets. The relationship between the information carried by different features was investigated using mutual information and illustrated by a graphical clustering tool.

Results

: The minimum number of features that can represent the whole feature set without performance loss was found to range between 5 and 11 for different datasets. In ear-EEG, 7 features based on Continuous Wavelet Transform (CWT) resulted in similar performance as the whole set whereas in two scalp EEG datasets, the difference between minimal CWT set and the whole set was statistically significant (0.008 and 0.017 difference in average kappa). Features were divided into groups according to the type of information they carry. The group containing relative power features was identified as the most informative feature group in sleep stage classification, whereas the group containing non-linear features was found to be the least informative.

Conclusions

: This study showed that EEG sleep staging can be performed based on a low dimensional feature space without significant decrease in sleep staging performance. This is especially important in the case of wearable devices like ear-EEG where low computational complexity is needed. The division of the feature space into groups of features, and the analysis of the distribution of feature groups for different feature set sizes, is helpful in the selection of an appropriate feature set.

中文翻译：

---

用于自动睡眠分期的低维特征空间研究

背景

：自动睡眠阶段分类在很大程度上取决于对一组良好的描述性功能的选择。然而，在不影响分类性能的情况下选择具有适当的低计算成本的特征集仍然是一个挑战。这项研究尝试使用最少的功能来表示睡眠脑电图模式，而不会造成明显的性能损失。

方法

：将三种特征选择算法应用于包含84个特征的高维特征空间。这些方法基于引导方法，该引导方法由Gini排名和功能之间的相互信息指导。在三个头皮脑电图（EEG）和一个耳朵EEG数据集上对算法进行了测试。使用互信息调查了不同功能所携带的信息之间的关系，并通过图形聚类工具进行了说明。

结果

：发现对于不同的数据集，可以代表整个功能集而不会造成性能损失的最少数量的功能在5到11之间。在耳式脑电图中，基于连续小波变换（CWT）的7个特征产生的效果与整个集合相似，而在两个头皮脑电数据集中，最小CWT集合与整个集合之间的差异具有统计学意义（平均差异为0.008和0.017） kappa）。根据要素所携带的信息类型将其分为几类。在睡眠阶段分类中，包含相对功率特征的组被认为是信息量最大的特征组，而发现包含非线性特征的组的信息量最少。

结论

：这项研究表明，可以基于低维特征空间执行脑电图睡眠分期，而不会显着降低睡眠分期表现。这对于需要低计算复杂度的可穿戴设备（如Ear-EEG）尤为重要。将特征空间划分为多个特征组，并分析不同特征集大小的特征组的分布，有助于选择合适的特征集。