基于希尔伯特变换和自适应阈值的R波检测算法

计算机与现代化 ›› 2022, Vol. 0 ›› Issue (02): 114-119.

基于希尔伯特变换和自适应阈值的R波检测算法

（1.上海理工大学健康工程与科学学院，上海200093；2.上海介入医疗器械工程技术研究中心，上海200093;
3.上海杨浦区市东医院，上海200090）

出版日期:2022-03-31 发布日期:2022-03-31
作者简介:郭田雨（1995—），女，安徽蚌埠人，硕士研究生，研究方向：信号处理与特征分析，E-mail: g1979787024@163.com; 严荣国（1975—），男，副教授，博士，研究方向：单片机，嵌入式系统，Android和iOS在医疗器械领域中的应用；徐玉玲（1997—），女，硕士研究生，研究方向：医疗电子仪器的设计。
基金资助:
上海介入医疗器械工程技术研究中心资助项目（18DZ2250900）; 上海理工大学医工交叉项目（1020308414）

Detection of R Wave Based on Hilbert Transform and Adaptive Threshold

(1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Shanghai Engineering Research Center of Interventional Medical Device, Shanghai 200093, China;
3. Shanghai Yangpu District Shidong Hospital, Shanghai 200090, China)

Online:2022-03-31 Published:2022-03-31

摘要/Abstract

摘要： 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。

关键词: 心电信号, R波检测, 希尔伯特变换, 自适应双阈值

Abstract: 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.

Key words: electrocardiogram signal, R wave detection, Hilbert transform, adaptive double threshold

郭田雨, 严荣国, 方旭晨, 徐玉玲, 陶争屹. 基于希尔伯特变换和自适应阈值的R波检测算法[J]. 计算机与现代化, 2022, 0(02): 114-119.

GUO Tian-yu, YAN Rong-guo, FANG Xu-chen, XU Yu-ling, TAO Zheng-yi. Detection of R Wave Based on Hilbert Transform and Adaptive Threshold[J]. Computer and Modernization, 2022, 0(02): 114-119.

参考文献［26］

［1］	中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2019概要［J］. 中国循环杂志, 2020,35(9):833-854.
［2］	XIE Q S, LI Y F, WANG G X, et al. An unobtrusive system for heart rate monitoring based on ballistocardiogram using Hilbert transform and Viterbi decoding［J］. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019,9(4):635-644.〖HJ0.55mm〗
［3］	JUN T J, PARK H J, MINH N H, et al. Premature ventricular contraction beat detection with deep neural networks［C］// Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA). 2016:859-864.
［4］	YE C, KUMAR B V, COIMBRA M T. Heartbeat classification using morphological and dynamic features of ECG signals［J］. IEEE Transactions on Bio-medical Engineering, 2012,59(10):2930-2941.
［5］	胡丹琴,蔡文杰. QRS复合波检测技术综述［J］. 中国医学物理学杂志, 2020,37(9):1208-1212.
［6］	张晓军,吴芝路. 基于改进差分阈值算法的心电检测技术研究［J］. 电子科技, 2020,33(2):6-13.
［7］	YANK H, DEIRMENCI E, BUYKAKLL B, et al. Electrocardiography (ECG) analysis and a new feature extraction method using wavelet transform with scalogram analysis［J］. Biomedical Engineering / Biomedizinische Technik, 2020,65(5):543-556.
［8］	PAL S, MITRA M. Empirical mode decomposition based ECG enhancement and QRS detection［J］. Computers in Biology and Medicine, 2012,42(1):83-92.
［9］	KIMJ H, PARK S E, JEUNG G W, et al. Detection of R-Peaks in ECG signal by adaptive linear neuron (ADALINE) artificial neural network［J］. MATEC Web of Conferences, 2016,54:1-4.
［10］	VIJAYARANGAN S， VIGNESH R, MURUGESAN B, et al. RPnet: A deep learning approach for robust R Peak detection in noisy ECG［C］// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in Conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. 2020:345-348.
［11］	BOLTON R，WESTPHAL L C. Preliminary results in display and abnormality recognition of Hilbert transformed e.c.g.s［J］. Medical and Biological Engineering and Computing， 1981,19(3):377-384.
［12］	管致中,夏恭袼,孟桥. 信号与线性系统［M］. 4版. 北京:高等教育出版社, 2004:191-195.
［13］	张异凡,王浩任,史浩天,等. 基于小波变换和希尔伯特包络分析的QRS波检测算法［J］. 计算机现代化, 2019(5):96-100.
［14］	李中健,李世锋,申继红,等. 心电图学系列讲座(五)——心电图各波段参考值［J］. 中国全科医学, 2014,17(5):604-606.
［15］	Aiwiscal. QRS心拍定位: 解决识别对象问题［EB/OL］. (2018-05-16)［2021-05-09］. https://blog.csdn.net/qq_15746879/article/details/80340671.
［16］	包志强,罗小宏,吕少卿,等. 自适应CA-CFAR的心电信号R波检测技术［J］. 信号处理, 2019,35(12):1959-1968.
［17］	QIN Q, LI J Q, YUE Y Y, et al. An adaptive and time-efficient ECG R-peak detection algorithm［J］. Journal of Healthcare Engineering, 2017,2017:598054. DOI:10.1155/2017/5980541.
［18］	ZALABARRIA U, IRIGOYEN E, MARTINEZ R, et al. Online robust R-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm［J］. Applied Mathematics and Computation, 2020,369:124839.
［19］	SMAOUI G,YOUNG A, ABID M. Single scale CWT algorithm for ECG beat detection for a portable monitoring system［J］. Journal of Medical and Biological Engineering, 2017,37(1):132-139.
［20］	MOODY G B, MARK R G. Development and evaluation of a 2-lead ECG analysis program［J］. Computers in Cardiology, 1982,9:39-44.
［21］	PLESNIKE, MALGINA O, TASIC J F, et al. Detection and delineation of the electrocardiogram QRS-complexes from phase portraits constructed with the derivative rule［J］. Experimental and Clinical Cardiology, 2014,20(8):2980-2989.
［22］	GHAFFARI A, HOMAEINEZHAD M R, ATAROD M, et al. Parallel processing of ECG and blood pressure waveforms for detection of acute hypotensive episodes: A simulation study using a risk scoring model［J］. Computer Methods in Biomechanics and Biomedical Engineering, 2010,13(2):197-213.
［23］	MARTINEZ J P, ALMEIDA R, OLMOS S, et al. A wavelet-based ECG delineator: Evaluation on standard databases［J］. IEEE Transactions on Bio-Medical Engineering, 2004,51(4):570-581.
［24］	姚易,廖晓阳,李志超. 计算机辅助诊断模型助力基层医疗机构诊断阵发性心房颤动［J］. 中国全科医学, 2021,24(2):143-147.
［25］	陈瑞. 心电房颤自动识别算法研究［D］. 武汉:湖北工业大学, 2020.
［26］	PAN J, TOMPKINGS W J. A real-time QRS detectionalgorithm［J］. IEEE Transactions on Bio-Medical Engineering, 1985,32(3):230-236.

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