心动周期信号的混沌特征分析及应用

ANALYSING CHAOTIC CHARACTERISTICS OF HEART PERIOD SIGNALS AND ITS APPLICATIONS

目的：研究心动周期信号（ＨＰＳ：Ｈｅａｒｔｐｅｒｉｏｄｓｉｇｎａｌ）的混沌特征和谱特征，及这些特征随年龄的变化。背景：描述心动周期信号的混沌特征的参数有：相对分散度（Ｈｒｄ），李雅普诺夫指数（Ｈｌｅ），分维数（Ｈｆｄ）以及我们提出的混沌度（Ｌｃｃ：ＣｈａｏｓｎｅｓｏｆＨＰＳ）。目前只有分维数与年龄关系的初步工作发表，未见有全面研究ＨＰＳ的混沌特征的工作报道。方法：将２６４例１４～６９岁健康自愿受试者分成６个年龄组（１０～１９，２０～２９，３０～３９，４０～４９，５０～５９，６０～６９），用本室研制的计算机化心动周期信号混沌分析系统进行测试。采用ＣＭ５导联，采样频率３００Ｈｚ，ＡＤ转换精度１２位，提取５１２个心动周期，计算Ｈｒｄ，Ｈｌｅ，Ｈｆｄ，Ｌｃ以及总功率（Ｔｆｐ）及三分段的绝对及相对功率（Ｐｖｖ，Ｐｌｖ，Ｐｈｖ，Ｐｖｒ，Ｐｌｒ，Ｐｈｒ）及平衡参数Ｒｖｈ，Ｒｌｈ（Ｒｖｈ＝Ｐｖｖ／Ｔｆｐ，Ｒｌｈ＝Ｐｌｖ／Ｔｆｐ），给出ＨＰＳ时域图，相平面图（ＰｈａｓｅＰｌａｎｅＰｌｏｔ），延迟映射图（Ｒｅｔｕｒｎｍａｐ）以及功率谱。结果：１四个混沌特征参数及功率谱的绝对及相对功率和总功率都随年龄的增加而降低，降低的重要原?

Chaotic and spectral characteristics of heart period signals(HPS) and variation of these characteristics with ages were studied. The self defined parameters describing chaotic characteristics of HPS were: relative dispersion(Hrd), Lyapunov exponent(Hle), Fractal dimension(Hfd) and chaosness(Lcc). At present, only report of primary work was reviewed on relation between fractal dimension and ages, but not report of studying comprehensively chaotic characteristics of HPS. 264 healthy volunteers were divided into six age groups(10～19,20～29,30～39,40～49,50～59,60～69 years old) and their HPS were tested by a computerized system for analysing chaotic characteristics of HPS made in our laboratory. CM5 lead, 300Hz sampling rate, 12 bits precision of ADC and 512 heart periods were adopted and Hrd, Hle, Hfd, Lcc, total power(Tfp), relative power in each of three frequency bands(0.0028～0.04Hz, 0.04～0.14Hz, 0.14～0.5Hz) (Pvr, Plr, Phr), absolute power in each of three frequency bands(Pvv, Plv, Phv), and balancing parameters(Rvh, Rlh) were computed. Time domain figure, phase plane plot, return map and power spectrum were given. Results: 1. The parameters of chaotic characteristics and power spectrum decreased with age increase. The main reason of the decrease was due to decrease of high frequency components reflecting activities of vagal nerve. 2. What decreased most rapidly were Phv and Phr in power spectrum parameters. 3. The fitted equations representing variation of each parameters with age, crossrelative equations and coherent coefficient among four chaotic characteristic parameters were obtained. 4. The balancing parameter Rvh increased with age increase. 5. Rlh increased first and then decreased. Discussion: 1. The decrease of chaotic characteristics of HPS with age increase reflected the decrease of autonomic nervous system function, as a result of decay of vagal nerve function. If there are some things able to protect vagal nerve and to improve its function effectively, it will be an important measure to resist the senescence. 2. In pathologic conditions, the vagal nerve might be damaged more easily. Chaotic characteristic parameters were more sensitive indexes than power spectrum parameters (such as balancing parameter Rvh) in respect of predicting and assessing the severity of disease.