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。 OBJECTIVE: To determine the RR interval during the analysis of electrocardiogram (ECG) plays a very important role in the analysis of ECG information. However, in clinical practice, the records of the actual test are inevitably disturbed by the outside world. Due to the existence of these interference information, it becomes very difficult to accurately locate the RR interval. The subject intends to extract the maximum information of ECG expression, to accurately locate the purpose of RR interval. Methods: In this study, the autocorrelation data processing method was used to effectively improve the difference between the main peak and the second peak intensity, which provided a possible method for better judging the RR interval and the QRS wave information buried in the noise. Results: We obtained the following information using the method of autocorrelation data processing: (1) The intensity ratio of the main peak to the next peak was increased from 2.7 times to 7.7 times for ECG with small interference. (2) The ECG with large disturbance, that is, the data that the main peak has been considered to be unusable by the existing Holter processing software and the physician’s manual judgment, because the intensity of the main peak of these data is obviously smaller than the intensity of the minor peak (main peak / minor peak <1 ). After our treatment, the ratio of the main peak intensity to the second peak intensity can be increased to 1.5 (main peak / sub-peak> 1.5), so that the RR interval can be clearly distinguished. CONCLUSIONS: When the ECG information is disturbed, its RR interval is difficult to judge. Using the data processing method of autocorrelation mode used in this study can improve the intensity ratio of the main peak and the second peak in the Holter monitor and improve the accuracy of the RR Accuracy of the period. Therefore, based on auto-correlation mode Holter RR interval data processing method is effective.