基于心率变异性非线性分析的冠心病中医证候研究
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摘要
目的
     中医证候是一个复杂的非线性系统,采用非线性方法研究中医证候是重要方向。近几年国内开始对人体自主神经状态和中医证候的相关性进行关注。HRV分析是目前唯一无创且定量检测自主神经功能的方法,包括线性分析法和非线性分析法,可以反映人体自主神经总能量、交感与迷走张力、以及交感与迷走平衡状态。课题组前期研究表明中医证候与自主神经功能有一定的相关性,表现为不同虚实状态下反映自主神经功能的HRV指标有显著差异,包括线性的时频域指标及反映混沌程度的非线性指标。在此基础上,我们采用小波变换技术将HRV信号分解到不同频段,对各频段信号分别进行非线性分析,并提取冠心病患者证候要素,将虚实证候进一步细化为具体的证候群,观察不同频段不同证候组间非线性指标的动态变化,阐述冠心病中医证候与HRV非线性指标之间的复杂关系及组合规律,探索冠心病不同中医证候的非线性特征以及所反映的人体病理生理学意义。
     方法
     观察246例年龄在40-70岁之间的慢性稳定型心绞痛患者。
     1.病例来源与观察对象:病例来源于2009年3月~2011年9月中国中医科学院望京医院心内科门诊及住院患者;以冠心病中最常见的慢性稳定型心绞痛患者为观察对象。
     2.慢性稳定型心绞痛诊断标准:采用2004年美国心脏病学会(ACC)/美国心脏学会(AHA)《慢性稳定性心绞痛诊疗指南》、2007年我国《慢性稳定型心绞痛诊断与治疗指南》中的相关内容作为诊断标准。
     3.纳入标准:年龄在40-70岁之间,符合下述条件之一者:
     (1)冠状动脉造影冠脉主支狭窄≥50%;
     (2)加拿大心血管学会心绞痛分级(CCSC)中Ⅰ-Ⅳ级稳定型心绞痛:
     (3)符合心电图心肌缺血诊断标准;
     (4)平板运动负荷试验结果阳性。
     4.排除标准:
     (1)急性心肌梗死;
     (2)其他疾病如肋软骨炎、肋间神经痛等所致胸痛,以及全身性疾病或心外疾病所致心电图改变或运动平板阳性者;
     (3)合并严重心肺疾病、肝肾功能不全、造血系统原发性疾病、精神病;
     (4)动态心电图检查前48h内服用洋地黄制剂、β受体阻滞剂;
     (5)严重心律失常,包括室性心动过速、房性心动过速、频发早搏、房室或束支传导阻滞、持续性心房颤动或心房扑动等。
     5.证候分类标准:根据1990年中国中西医结合学会心血管学会修订的《冠心病中医辨证标准》对冠心病病例进行中医证候辨证分型,标准如下:
     5.1实证类
     痰浊:胸脘痞满、苔厚腻、脉滑;痰浊偏寒苔白厚腻;痰浊偏热苔黄厚腻,或脉滑数。
     血瘀:胸痛、痛有定处、舌质紫暗或有瘀点瘀斑。
     气滞:胸闷痛、憋气、苔薄。
     寒凝:胸痛甚、遇寒常发。
     5.2虚证类
     气虚:其共性表现为疲乏、气短、舌质淡胖嫩或有齿痕,脉沉细。(1)心气虚:气虚兼有心悸者。(2)脾气虚:气虚兼有腹胀食少者。(3)肾气虚:气虚兼有头晕目眩、健忘耳鸣、腰膝酸软者。
     阳虚:其共性表现为疲乏、气短、身寒、肢凉、舌淡胖或有齿痕,脉沉细或迟。(1)心阳虚:阳虚兼有心悸者。(2)肾阳虚:阳虚兼有腰膝酸软、肿胀、夜尿频多者。
     阴虚:其共性表现为舌红少苔或无苔、或五心烦热、口干、脉细数。(1)心阴虚:阴虚兼有心悸者。(2)肾阴虚:阴虚兼有头晕、目眩、耳鸣、腰膝酸软、健忘者。
     阳脱:四肢厥冷、大汗出、脉微欲绝,表情淡漠、面色晀白或暗淡或浮红,舌质暗淡。
     6.观察指标
     (1)HRV非线性参数:复杂度、近似熵、关联维数(小波变换后高频、低频、极低频、超低频4个不同频段的非线性参数)。
     (2)24小时动态变化趋势:上述各项非线性参数指标在昼夜24小时中的动态变化趋势。
     7.病例采集与分组:所有纳入的患者,在医生指导下填写“四诊调查表”,根据中医辨证标准分为实证、虚证、虚实夹杂证三组。
     8.心电信息采集与HRV数据提取:
     用动态心电记录仪记录每个入选患者日常状态下的24小时长程连续心电信号,输入动态心电分析仪,经自动分析之后,采用“人机对话”进一步修改数据,直至干扰信号全部剔除。
     对来自于动态心电分析仪输出端的原始HRV信息用小波变换,对HRV信息中混杂在一起的多频段信号分频段提取,以备在不同频段中对各项观察指标进行分析。
     9.指标分析方法:(1)非线性参数计算:对经小波变换后的HRV数据信息,用课题组自制的专用软件,依托课题组成员单位首都医科大学生物医学工程学院专业人员,分时段(每小时为一段)计算复杂度、近似熵、关联维数值;(2)制作上述指标在24小时中的动态变化趋势图。
     10.数据录入:采用Excel数据库,录入中医证候分类信息和HRV各项非线性指标测量和计算结果。
     11.统计学方法:采用SPSS16.0,用方差分析和秩和检验对冠心病不同证候组间的各项非线性指标进行比较。用SAS9.3系统,采用Kmeans聚类方法,依据四诊信息及证候要素分析,对246例冠心病患者进行证候分类。然后建立线性混合效应模型,采用时间、证候,以及证候与时间交互效应来分别对复杂度、近似熵、关联维进行分析,考察不同证候组间非线性参数的差异。
     12.作图方法:采用Excel软件绘制非线性参数波动趋势图。
     结果
     1.冠心病患者证候要素分布:以气虚为主,占31.13%,其次分别为痰浊23.75%,阴虚22.55%,气滞18.16%,阳虚13.57%,血瘀10.77%。虚实证候分布:实证43例(17.5%),虚实夹杂证153例(62.2%),虚证50例(20.3%)。
     2. Kmeans聚类分析后,结合证候要素分析,246例冠心病患者具体证候分10类,分别为:气虚血瘀兼痰浊证,痰浊证,气虚证,痰瘀互阻证,气阴两虚兼气滞证,气阳虚兼气滞证,气阴两虚兼痰浊证,阴阳两虚兼气虚痰浊证,气阴两虚证,气阳虚证。
     3.一昼夜中,无论患者与正常人,人体混沌程度都有周期性变化,表现为在不同时刻有规律地出现高峰与低谷,正间正午时段HRV非线性指标降低,表明混沌程度降低;夜间时段,HRV非线性指标都较日间高,表明混沌程度增高。
     4.虚、实证候组间比较,多数非线性指标在组间有显著差异,表现为实证组混沌程度最高,虚证组最低,且非线性指标呈动态变化趋势。
     5.在所有的复杂度、近似熵、关联维这三项指标中,复杂度与近似熵指标的统计结果显示有相同趋势,其中复杂度对虚实证候的区分度和差异最为敏感,而关联维差异不明显。
     6.多元分析提示,虚实证候间复杂度、近似熵的差异具有显著性,实证组的复杂度、近似熵指标较高,虚证组指标较低。对不同证候组非线性指标的时间序列分析结果显示:复杂度24小时波动曲线近似“V”形,在10-15点处于最低谷;近似熵24小时波动曲线呈近似“S”形,在9-12点处于波谷,15-21点处于波峰;关联维24小时波动曲线呈近似“V”形,在9-12点处于波谷。
     7.证候细化后,冠心病中医证候多属虚实夹杂,多元分析显示不同证候组间非线性参数的差异不明显。
     结论
     1.一昼夜中,人体混沌指标在不同时刻有规律地出现高峰与低谷,提示人体HRV混沌程度的变化与人体阳气的盛衰有关,“日中而阳气隆”,消耗能量,混沌程度降低,夜间“阳气入内”,能量内守,混沌程度恢复。
     2.在一昼夜中,不同频段下虚、实证候的多项指标变化趋势都有动态变化,同时多数指标值在组间有显著差异,表现为实证组混沌程度最高,虚证组最低。提示实证组自主神经调节能力强,对外界适应能力强,抗邪能力强;虚证组自主神经调节能力弱,对外界适应能力弱,抗邪能力差。
     3.在复杂度、近似熵、关联维这三项指标中,复杂度与近似熵指标的统计结果显示有相同趋势,其中复杂度对虚实证候的区分度和差异最为敏感,而关联维差异不明显。提示在分析HRV的混沌程度上,复杂度应作为比较理想的首选指标,其次是近似熵。
     4.在HF、LF、VLF、ULF四个频段的混沌指标中,HF指标最敏感,而VLF、ULF敏感性差。HF指标反应迷走神经张力,提示实证组患者迷走张力较高,对心血管系统保护作用强,预后较好。
     5.冠心病临床以虚实夹杂证为主,多元分析不同证候间非线性参数的差异不明显,提示进一步完善证候分类方法,尝试不同数学建模方法,挖掘能揭示不同证候区别的非线性参数,研究临床常见证候的非线性特征。
Objective:HRV analysis can reflect the autonomic nervous function. Preliminary studies show that traditional Chinese medicine syndrome is correlated with autonomic nervous function. In this study, by observing the dynamic changes of the different syndromes of nonlinear parameters, we described HRV nonlinear indicators of coronary heart disease TCM syndromes and complex relationships and the rule of combination, and explore the Nonlinear analysis method for Coronary Heart Disease Syndromes.
     Methods:The study discriminated the traditional Chinese medicine syndrome of246cases patients with coronary heart disease, at the same time collecting their24hours long-range heart rate variability(HRV) data by using24-hour Holter recorder, and calculated energy spectrum of HRV nonlinear indexes (including complexity, approximate entropy and correlation dimension)in four different frequency band(including high frequency, low frequency, ultralow frequency and very low frequency),to observe the characteristics and differences of each index among the three groups which are deficiency syndrome and excess syndrome and deficiency-excess complication syndrome group. Then classified syndromes by clustering method, combined with the analysis of the performance of the clinical syndrome, made multivariate statistical analysis of coronary heart disease syndromes and HRV nonlinear parameter information.
     Results:(1) At midday the energy spectrum of most HRV indexes are lower, which shows that chaos degree is reduced; while the energy spectrum of most HRV indexes are higher at night, which showed that chaos degree increase.(2) Many indexes trends of deficiency syndrome and excess syndrome have dynamic change all day. Compared with deficiency syndrome, the excess syndrome group is always in high energy condition in different times.(3) Complexity and approximate entropy have the same trend in different indexes. In these indexes, complexity is most sensitive, the second is approximate entropy, and the correlation dimension is not sensitive.(4) In four frequency band, the indexes in high frequency band are most sensitive, these in low frequency is the second, while the indexes in ultralow frequency and very low frequency is poor sensitive.(5) Multivariate analysis suggest that complexity and approximate entropy can discriminate excess syndrome from deficiency syndrome, while correlation dimension is not sensitive.(6)After syndrome refinement, we did not find significant difference of the nonlinear parameters between the different syndromes, showed that The nonlinear parameters related with the "excess" and "deficiency", body's "healthy atmosphere".
     Conclusion:HRV high-frequency energy reflect the human vagal tone, the level of high frequency energy can reflect the degree of chaos of the human body,"excess" is related to the keep of HRV nonlinear high frequency energy, and "deficiency" is related to the loss of HRV nonlinear high frequency energy, which indicate that this index may help determine how many the body's "healthy atmosphere" is and the degree of ups and downs and the extent of deficiency and excess.
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