基于希尔伯特黄熵的麻醉深度估计  

Estimation of Anesthetic Depth with Hilbert-Huang Entropy

作　　者：李小俚[1] 崔素媛[1] Sleigh J W 

机构地区：[1]燕山大学电气工程学院网络控制和生物信息中心,秦皇岛066004 [2]Department of Anesthesia,Waikato Hospital,Auckland University,New Zealand

出　　处：《中国生物医学工程学报》2008年第5期689-694,共6页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(60575012)

摘　　要：麻醉深度监测是外科手术中必不可少的步骤之一。目前已经提出多种监测麻醉深度的脑电信号分析方法,尤其熵方法得到了广泛的关注。提出一种新的麻醉深度监测方法-希尔伯特黄熵,先用经验模态分解—希尔伯特黄变换处理脑电信号获取希尔伯特黄边际谱,再根据香农熵定义得到希尔伯特黄熵。对19个接受吸入药物七氟醚麻醉的病人脑电信号的希尔伯特黄熵和时频均衡谱熵进行计算、测试和比较,结果表明:希尔伯特黄熵能够更准确的区分麻醉和清醒状态,更适合于麻醉深度监测。Estimation of anesthetic depth is very important in surgery.Several methods have been proposed to estimate the depth of anesthesia（DoA） by analyzing EEG signals,in particular entropy method was paid more attentions.In this paper,we addressed a new method to estimate the depth of anesthesia based on Hilbert-Huang transforms.The method was composed of two steps.First,empirical mode decomposition and Hilbert transforms were combined to decompose EEG signals for obtaining a marginal power spectrum;then the spectral entropy-like calculation was employed to obtain the Hibert-Huang entropy based on marginal power spectrum.The EEG signals were collected from 19 patients with sevoflurane,the comparison between the Hilbet-Huang entropy and M-entropy showed that the Hilbert-Huang entropy was better to distinguish the conscious from unconscious states.

关 键 词：麻醉深度 经验模态分解 希尔伯特黄变换 熵 脑电信号 

分 类 号：R318.04[医药卫生—生物医学工程]