摘要
研究对象为白天短时睡眠时记录下来的多导睡眠生理数据,主要是为了提取睡眠过程中出现的睡眠各阶段的特征,并实现自动分期。首先,同步采集了白天20~30 min的短时睡眠过程中的脑电图（EEG）等生理数据;然后利用快速傅里叶变换（FFT）对采集到的数据进行频谱分析,提取睡眠各阶段的频域特征;最后采用支持向量机对短时睡眠数据进行自动分期。实验结果表明：FFT结合支持向量机（SVM）在短时睡眠阶段的研究中能够得到较好的分期结果。因此,通过对短时睡眠过程中浅睡眠各阶段的特征和分类结果的分析,能够为短时睡眠提供客观评价的依据。
The physiological data recorded during the day time short sleep were analyzed.The objective was to extract the characteristics of sleep stages and realize the automatic determination of sleep stages.Firstly,electroencephalography（EEG） and other physiological data of 20~30 min day time were acquired synchronously.Secondly,fast Fourier transform（FFT） was utilized for the spectral analysis and feature extraction.Finally,support vector machine（SVM） was adopted for automatic determination for short-time sleep data.It was shown from the experimental results that FFT with SVM can achieve better results in the study of short-time sleep stages.Hence,the obtained feature extraction and classification results can be utilized as the assistant information for day time short sleep assessment.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2011年第1期84-89,共6页
Journal of East China University of Science and Technology
基金
国家自然科学基金(60674089)
上海市重点学科建设项目(B504)
中央高校基本科研业务费专项资金项目(WH0914028)
