| 彭振 韦明 郭建平 肖蒙 王迎雪.基于奇异值第一主成分的睡眠脑电分期方法研究[J].,2014,14(7):1368-1372 |
| 基于奇异值第一主成分的睡眠脑电分期方法研究 |
| Study of Sleep EEG Staging Method Based on theFirst Principal Component of Singular Value |
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| DOI： |
| 中文关键词: 脑电 睡眠分期 奇异值第一主成分 抗噪 |
| 英文关键词: EEG Sleep staging The first principal component of singular value Restrain noise |
| 基金项目:国家自然科学基金项目(31070914) |
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| 中文摘要: |
| 目的：脑电信号含多种噪声和伪迹，信噪比较低，特征提取前必须进行复杂的预处理，严重影响睡眠分期的速度。鉴于此，本
文提出一种基于奇异值第一主成分的睡眠脑电分期方法，该方法抗噪性能较强，可省去预处理过程，减少计算量，提高睡眠分期
的效率。方法：对未经过预处理的睡眠脑电进行奇异系统分析，研究奇异谱曲线，提取奇异值第一主成分，探索其随睡眠状态变化
的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果：奇异值第一主成分不仅能表征脑电信号主体，而且可以抑
制噪声、降低维数。随着睡眠的深入，奇异值第一主成分的值逐渐增大，但在REM 期处于S1 期和S2 期之间。经MIT-BIH 睡眠数
据库中5 例同导联位置的脑电数据测试（仅1 导脑电数据），睡眠脑电分期的准确率达到86.4%。结论：在未对脑电信号进行预处
理的情况下，提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态，是一种有效的睡眠分期依据。本文运用提出的方法仅采
用1 导脑电数据，就能得到较为满意的睡眠分期结果。该方法有较强的分类性能，且抗噪能力强，不需要对脑电作复杂的预处理，
计算量小，方法简单，很大程度上提高了睡眠分期的效率。 |
| 英文摘要: |
| Objectivr:EEG signal contains a variety of noise and artifacts, it has low SNR. The complex pre-processing must be
done before the feature extraction. It will seriously affect the speed of the sleep staging. In view of this, a sleep staging method based on
the first principal component of singular value is proposed in this paper. This method is robust to noise. The need for pre-processing is
eliminated to reduce the amount of computation and improve the efficiency of sleep staging.Methods:By singular value decomposition
(SVD) on the EEG without pre-processing, study the singular spectrum curve. And extracting the first principal component of singular
value on EEG to explore the rule with the change of sleep states. The SVMis used for sleep EEG stage determination.Results:The first
principal component of singular value can not only characterize the sleep states, but also can restrain noise and reduce dimension. Along
with the deepening of sleep, the first principal component of singular value gradually increases its value, but between S1 and S2 in REM.
Be tested on the 5 cases EEG data with the same channel position in the MIT-BIH database (one channel data only), the accuracy that
using the proposed scheme achieves 86.4%.Conclusion:The first
principal component of singular value can not only characterize the sleep states, but also can restrain noise and reduce dimension. Along
with the deepening of sleep, the first principal component of singular value gradually increases its value, but between S1 and S2 in REM.
Be tested on the 5 cases EEG data with the same channel position in the MIT-BIH database (one channel data only), the accuracy that
using the proposed scheme achieves 86.4%. |
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