摘要
目的：利用小波转换方法对阻塞性睡眠呼吸暂停低通气综合征（OSAHS）患者脑电信号进行处理分析。方法：对人选142例鼾症患者分为对照组、轻度组、中度组和重度组。利用Morlet小波函数对脑电信号进行变换处理得到平均能量尺度图，通过对单位时间能量的计算得到脑电能量变异的两个指标divl和div2，并分别提取两个特征参数P1、P50和P10（Pdiv2≥1．0）、P20（Pdiv2≥1.0）。检验各个指标的正态分布性和各指标在各样本组间的差异性，以及各指标与临床指标的相关性。结果：发现变异指标的四个参数各组之间的检验结果均表现出相似的规律：重度组与对照、轻、中度组之间的差异均有显著意义（P〈0．05）；其它各组之间的差异无显著意义，但有显著性趋势（0．05〈P〈0．1）；各参数与多项睡眠图（PSG）检测指标的多元回归分析结果显示：与参数P1、P50、P10（Pdivz≥1.0）、P20（Pdiv2≥1.0）相关性最强的一个指标分别为呼吸暂停总时间／睡眠总时间（TAT／TST）、呼吸暂停指数、指端血氧饱和度（SpO2）％90％时间／TST、呼吸暂停指数（标准化偏回归系数分别为-0．369、-0．720、0．317、-0．602，P均〈0．05）。结论：脑电能量变异性的定量指标div1和div2的四个参数P1、P50和P10（Pdiv2≥1.0）、P20（Pdiv2≥1.0）表现出随病情加重而变化的趋势，并且对重度OSAHS有一定的区分意义；呼吸暂停是影响脑电能量变化的主要因素。
Objective： To analyse the relationship between the index from EEG signals wavelet transformation and the serious degree of OSAHS and explore the value of wavelet transform processing on EEG signals of OSAHS patients. Methods： All subjects were monitored by polysomnograpgy（PSG）, and they were divided subsequently into the control （non-OSAHS）, mild, moderate and severe groups according to their AHI. Firstly EEG data was transformed to EDP form, and then Morlet wavelet func- tion was used in the wavelet transformalion to process the EEG signals associated with sleep breathing. Based on the wavelet coefficient diagram in the frequency time domain, some indexes related to the aver age energy were proposed to describe the serious degree of OSAHS in the patients. Results： Compared with controls,among the mild and moderate and severe groups OSAHS had significant defferenee in indexes P1 ,P50 ,P10 （Pdiv2≥1.0） and P20 （Pdiv2≥1.0） that derived from div1 and div2 （P〈0. 05） . In the multi ple regression analysis between EEG indexes and clinical parameters,the most related element about P1 , P50 ,P10 （Pdiv2≥1.0）and P20 （Pdiv2≥1.0） was TAT/TST, apnea index, SpO2 （90%T/TST）and apnea index, -0. 602 respectively（regression coefficients were -0. 369, -0. 720, 0. 317,- 0. 602 respectively, all （P d0.05）. Conclusion： OSAHS EEG average energy indexes div 1 and div 2 changed with serious de gree of OSAHS and they may contribute to the identification of different degrees in patients with OSAHS especially in the severe group. All parameters（except for P10 ） are highly related to apnea indexes. Statistical analysis showed that wavelet transform is a good tool for the research of OSAHS EEG signals.
出处
《癫痫与神经电生理学杂志》
2011年第5期273-280,共8页
Journal of Epileptology and Electroneurophysiology(China)
基金
北京市科技计划基金（课题编号：Z005190041691）
致谢：本文承蒙北京大学第一医院神经科吴逊教授的指导,特此致谢!
