【摘要】 睡眠分期是研究睡眠及相关疾病的基础,是完成睡眠质量评估的前提。为实现有效睡眠自动分期,本文提出将能量特征和最小二乘支持向量机(LS-SVM)相结合的方法。先利用FIR带通滤波器提取Pz-Oz导睡眠脑电信号的特征波,获得能量特征,并与小波包变换方法相比较;然后用LS-SVM分类器进行模式识别,最终实现睡眠自动分期。实验表明,本文所提出的基于能量特征和LS-SVM的自动睡眠分期方法简单、有效,平均正确率达88.89%,具有很好的应用前景。更多还原

【Abstract】 The research of sleep staging is not only the basis of diagnosing sleep related diseases,but also the precondition of evaluating sleep quality,and has important clinical significance.In recent years,the research of automatic sleep staging based on computer has become a hotspot and made some achievements.Feature extraction and feature classification are two key technologies in automatic sleep staging system.In order to achieve effective automatic sleep staging,we proposed a new automatic sleep staging method which combines the energy features and least squares support vector machines(LS-SVM).Firstly,we used FIR band-pass filter to extract the energy features of Pz-Oz channel sleep electroencephalogram(EEG)signals,and compared them with those from wavelet packet transform method.Then we designed an LS-SVM classifier to realize the automatic sleep stage classification.The research showed that FIR band-pass filter(with the Kaiser window)performed better than wavelet packet transform(WPT)for energy feature extraction just in terms of the data from the Sleep-EDF Database and the LS-SVM classifier(with the RBF Kernel function)designed was good,and the automatic sleep staging method proposed in this paper was better than many similar methods from other studies with an average accuracy of 88.89% and had a very prosperous application future.更多还原