基于希尔伯特变换和自适应阈值的R波检测算法

提出一种基于希尔伯特变换和自适应双阈值的R波检测算法。首先对预处理后的信号进行幅度归一化和希尔伯特包络分析；然后采用自适应双阈值法检测R波；最后，根据增强后的信号定位检测到R波的位置。使用4个具有不同频率和信噪比的数据库（MIT-BIH心率失常数据库、QT数据库、NST噪声数据库、European ST-T数据库）和临床采集心电数据对所提算法进行性能评估，结果表明，各种不规律和含有严重噪声干扰的心电信号中R波的位置依然能被所提算法准确检测出。在MIT-BIH心律失常数据库中，总体数据检测的敏感性、阳性检测度和准确率分别达到了99.36%、99.77%和99.13%，每条记录平均消耗时间比传统的Pan and Tompkins算法大大缩短。实验结果表明该算法具有良好的鲁棒性和实时性。

Detection of R Wave Based on Hilbert Transform and Adaptive Threshold

This paper presents a R wave detection algorithm based on Hilbert transform and adaptive double threshold.Firstly,amplitude normalization and Hilbert envelopment analysis are performed on the pre-processed signal.Then,the R wave is detected by the adaptive double threshold methods.Finally,the location of the detected R wave is located according to the enhanced signal.4 kinds of databases with different frequencies and signal noise ratio,like MIT-BIH Arrhythmia,QT,NST,European ST-T,and clinical collection of ECG data are used to evaluate the performance of the proposed algorithm.The Results show that the location of R wave in various irregular ECG signals with serious noise interference can still be accurately detected by the proposed algorithm.It has the sensitivity,positive and accuracy of the overall data detection reached 99.36%,99.77%and 99.13%in the MIT-BIH arrhythmia database,and compared with the traditional Pan and Tompkins algorithms,the average consumption time of each record is greatly reduced,which proves that the proposed algorithm has good robustness and real-time performance.