中文摘要：

目的：在动态心电图分析过程中,确定RR间期,对于分析心电信息起着非常重要的作用。但是,临床上,实际检测的记录中,不可避免地受到外界很多的干扰,由于这些干扰信息的存在,使得准确定位RR间期变得非常困难。本课题拟在干扰情况下,提取心电表达的最大信息,达到准确定位RR间期的目的。方法：本研究运用自相关模式数据处理方法有效地提升了主峰、次峰强度间的差别,从而为更好地判断RR间期以及埋藏在噪音之中的QRS波信息提供了可能的方法。结果：我们用了自相关模式数据处理的方法获得了以下信息：（1）对于干扰小的心电信息,主峰与次峰间的强度比值由2.7倍提升到7.7倍。（2）对于干扰大的心电信息,即那些主峰已经被现有Holter处理软件及医生人工判断都认为不可以使用的数据,因为这些数据主峰强度明显小于次峰强度（主峰/次峰〈1）,经过我们的方法处理后,可以使主峰强度与次峰强度之比提升到1.5（主峰/次峰〉1.5）,从而使得RR间期可以进行清晰分辨。结论：在心电信息受到干扰的情况下,它的RR间期很难判断,运用本研究使用的自相关模式数据处理方法,能够提升动态心电图中主峰与次峰的强度比值,提高人工判断RR间期的准确性。所以,基于自相关模式的动态心电图RR间期数据处理方法是行之有效的。

英文摘要：

Objective： It is very important to determine the RR interval in the process of the ambulatory electrocardiography analysis. However, it is inevitable to suffer interference when measuring DCG in clinical circumstances. The purpose of this article is to determine RR interval in order to extract maximum DCG signals. Methods： In this research, the method of autocorrelation data processing is used. The method can effectively enhance the difference between the intensity of main peak and secondary peak, so that we can better determine the RR interval or the information of QRS wave buried in the noise, which can achieve the purpose of positioning RR interval accurately. Results：（1） For electrocardiosignal suffering from small interference, we promote its ratio of main peak intensity and secondary peak intensity from 2.7 times to 7.7 times.（2） For electrocardiosignal suffering from large interference, we promote its ratio of main peak intensity and secondary peak intensity from less than 1 time to more than 1.5 times by using autocorrelation processing. Conclusion： It is very difficult to determine the RR intervals when dynamic ECG signals suffering from interference. The method of autocorrelation data processing can enhance the ratio of the main peak intensity and the secondary peak intensity, which can achieve the purpose of positioning RR interval accurately in DCG analysis. It is effective to determine the RR interval by using autocorrelation method.