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心电信号特征波的准确检测是心电信号自动分析和诊断的关键,其中QRS波群的R波识别更是计算心率,区分心率失常及心率变异性分析的前提和基础。本文在提升小波算法的基础上,对传统的差分阈值算法进行了改进,通过对R波第一检测点及时间窗宽度的精细优化,使得本文改进后的差分阈值算法具有更好的实时性及更强的R波识别率。使用MIT-BIH标准心律失常数据库的心电信号数据作为样本数据进行实验,实验表明本文改进后的差分阈值算法能够准确检测R波的特征值,R波识别率高且明显优于传统的差分阈值算法。
The accurate detection of ECG characteristic wave is the key to the automatic analysis and diagnosis of ECG signal. The R wave identification of QRS wave group is the prerequisite and basis for the analysis of heart rate and heart rate variability. In this paper, based on the improved wavelet algorithm, the traditional differential threshold algorithm is improved. By finely optimizing the first detection point and the time window of R-wave, the improved differential threshold algorithm in this paper has better real- More R wave recognition rate. Experiments using the ECG data of MIT-BIH standard arrhythmia database as sample data show that the improved differential threshold algorithm in this paper can accurately detect the eigenvalues of R wave, and the recognition rate of R wave is higher and better than the traditional differential threshold algorithm.