本文利用正常心脏和病变心脏电活动模型以及躯干模型建立了产生常规心电图和高频心电图的数学物理问题，找出了用计算机解决这类数学物理问题的途径，并且用计算机模拟出正常心脏和异常（有病灶）心脏，包括病灶位置变化、病灶大小不同以及病灶不同严重程度等情况下，心电波图形的变化，显示出QRS波上高频切迹和扭结对病灶特性的敏感性和特鼻性，阱究掰频心电图QRS波上的切迹、扭结的大小和分布状态与心脏疾病的内在联系。

本文叙述了一种用NEC PC-8000个人计算机系统对高额心电图进行采样。处理的方法，应用屯子技术和一些信号处理于段。成功地将高狠心电信号提取出来。通过近5O例人体宴验，得出了一些有关正常人和心脏病人高频心电图复杂程度不同的韧步结论+这对临床诊断具有重要意义，在系统软件中，使用了多种计算机语言。

小渡变换是一种线性运算，它对信号进行不同足度的分解，可有救地应用于如信噪分离。提高时频两域的分辨率等讨论小渡变换用于心电QRS波形高频成份特征提取的方法实验结果表明高频截止频率 和w值，对于心肌缺血（冠心病）者其值普遍高于无心肌缺血（冠心病）者。并且揭示了急性心肌缺血时心电信息的高频成份动态变化的本质。

介绍一种神经网络一模糊推理协作系统神经阿络有很好的学习能力和准确拟台任意非线性函数的能力。模糊模型将专家知识转换到神经同络中，然后把它应用于诊断系统。从而使得心血管疾病（心肌缺血）的诊断准确率大大提高。

设计高保真12通道放大器，同步检测分析12导联心电图是符合国际心电学发展方向的一种新的检测分析方法，该方法可同步整体观察和识别12个导联同一心动周期的波形，提高各种测量、分析计算的准确性，降低目前存在的心电图测量计算的变异性，在临床诊断中具有重要意义。提出采用微机并行口的EPP协议设计A/D卡，实现心电数据采样和传输，既简单易用又使多种设备共享同一个接口，依据不同的软件实现不同的功能，鲁棒性好。使检测分析系统的整机性能更稳定、可靠。

Physiological systems generate complex fluctuations in their output signals that reflect the underlying dynamics. The base-scale entropy method was proposed as a complexity measure to investigate the complexity of time series. The advantages of this method are simplicity and extremely fast calculation for very short data sets. This method enables analyzing very short, nonstationary, and noisy data sets. We employed this method for short-term physiological time series for analysis of heart-rate variability signals. The results show that the simple and easily calculated measure can effectively detect the complexity dissimilarity of physiological time series in different physiological or pathological states, which is convenient for clinical applications.

We have analyzed cardiac ische. mia-reperfusion in an animal model using epicardial electrODOtenlial mapping. We investigated the rela. tionship between ischemia and variability of multifractality in epicardial electrograms. We present a new parameter called the singularity spectrum area reference dispersion (SARD) that clearly demon. strates the change in multifractility with the extent of myocardiaischemia. By contrasting the 3D ventricular epicardial SARD map with the activation map, we conclude that myocardiaI ischemia significantly influences the variety of multifractality of ventricular epicardium electrograms and the SARD parameter is usefuIin correlating multifractality of epicardiaI elec. trograms with Iocation of ischemia closely.

Nonlinear science research is a hot point in the world. It has deepened our cognition of determinism and randomicity,simplicity and com-plexity, noise and order and it wilI profoundly influence the progress of the study of naturaI science, including life science. Life is the most complex nonlinear systern and heart is the core of lifecycle systern. In the Iate more than 20 years. nonlinear research on heart elect ric activities has made much headway. The commonly used parameters are based on chaos and fractaI theow, such as correlation dimension, Lyapunov ex-ponent, Kolmogorov entropy and multifractal singu-larity spectrum. This paper summarizes the commonly used methods in the nonlinear study of heart electric signal Then, considering the shortages of the above tradi-tionaI nonlinear parameters. we mainly introduce the results on short-term heart rate variability (HRV) signaI(500 R-R intervals)and HFECG signaI(1-2s). Finally,we point out it is worthwhile to put em phasis on the study of the sensitive nonlinearity parameters of short-term heart electric signaI and their dynamic character and clinicaI efectivity.

This paper proposes that a multiscale multifractality MSMF method be adopted for the spatiotemporal analysis of 12-lead ECG. By using this method, the authors find that, in some frequency range, 12-lead ECG has a more complex fractal structure, and the position of the largest singularity strength range ∆α is not relying on the data length but on the scale factor. By determining the inflexion, the MSMF proves to be more sensitive in displaying the trend that the singularity strength range ∆α of human ECG decreases with human aging.

An improved approximate entropy (ApEn) algorithm based on wave mode is proposed by analyzing and modifying ApEn, so that the irregular analysis can be applied to analyze the short-term series, which contain a great deal of detailed information and fluctuate slowly but in a wide range, such as high-frequency electrocardiogram (HFECG). By analyzing the complexity of HFECG, a conclusion can be drawn that ApEn algorithm based on wave mode can obviously distinguish heart diseases from the healthy group. Therefore, it is of significance for diagnosing myocardial infarction in time.