基于香农能量与自适应阈值的心电QRS复合波检测算法

doi:10.11772/j.issn.1001-9081.2019050818

计算机应用 ›› 2020, Vol. 40 ›› Issue (1): 304-310.DOI: 10.11772/j.issn.1001-9081.2019050818

• 应用前沿、交叉与综合 • 上一篇

基于香农能量与自适应阈值的心电QRS复合波检测算法

王治忠, 李泓毅, 韩闯

郑州大学电气工程学院, 郑州 450001

收稿日期:2019-05-14 修回日期:2019-07-17 出版日期:2020-01-10 发布日期:2019-07-25
作者简介:王治忠(1982-),男,山东蓬莱人,副教授,博士,主要研究方向:生物信号检测与处理;李泓毅(1994-),男,河南周口人,硕士研究生,主要研究方向:心电信号分析与智能诊断;韩闯(1991-),男,河南驻马店人,博士研究生,主要研究方向:心电信号分析与智能诊断。
基金资助:
国家自然科学基金资助项目（61673353）；国家自然科学基金青年科学家基金资助项目（61603344）；河南省高等教育重点研究项目（15A120017）。

QRS complex detection algorithm of electrocardiograph based on Shannon energy and adaptive threshold

WANG Zhizhong, LI Hongyi, HAN Chuang

College of Electrical Engineering, Zhengzhou University, Zhengzhou Henan 450000, China

Received:2019-05-14 Revised:2019-07-17 Online:2020-01-10 Published:2019-07-25
Contact: 韩闯
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61673353), the National Natural Science Foundation of China Young Scientists Fund (61603344), the Henan Province Higher Education Key Research Project (15A120017).

摘要/Abstract

摘要： 针对现有心电QRS复合波检测算法对于一些信号异常的情况检测效果仍然不理想的问题，提出了一种基于香农能量与自适应阈值相结合的心电QRS复合波检测算法，以解决QRS复合波检测的低准确率问题。首先，从预处理后的信号提取香农能量包络；然后，结合改进的自适应阈值方法对QRS复合波进行检测；最后，根据QRS复合波增强后的信号定位所检测的QRS复合波的位置。使用MIT-BIH心律失常数据库的数据对所提算法进行性能评估，结果表明，所提算法即使在信号中存在高大的P波、T波、不规则心律以及严重的噪声干扰时依然能准确检测QRS复合波的位置，总体数据检测的敏感性、阳性检测度和准确率分别达到了99.88%、99.85%和99.73%，且该算法能够在保证准确率的情况下快速地完成QRS复合波的检测任务。

关键词: 心电信号, 去噪, 香农能量, 自适应阈值, QRS复合波检测

Abstract: In view of the problem that the existing QRS complex detection algorithms of electrocardiograph are still not ideal for the detection of some signal abnormalities, a QRS complex detection method combining Shannon energy with adaptive threshold was proposed to solve the problem of low accuracy of QRS complex detection. Firstly, the Shannon energy envelope was extracted from the pre-processed signal. Then, the QRS complex was detected by the improved adaptive threshold method. Finally, the location of the detected QRS complex was located according to the enhanced signal of the detected QRS complex. The MIT-BIH arrhythmia database was employed to evaluate the performance of the proposed algorithm. Results show that the algorithm can accurately detect the location of the QRS complex even when high P wave, T wave, irregular rhythm and serious noise interference exist in the signal, and has the sensitivity, positive and accuracy of the overall data detection reached 99.88%, 99.85% and 99.73% respectively, meanwhile the proposed algorithm can quickly complete the QRS complex detection task with the accuracy guaranteed.

Key words: electrocardiogram signal, denoising, Shannon energy, adaptive threshold, QRS complex detection

中图分类号:

TP391

王治忠, 李泓毅, 韩闯. 基于香农能量与自适应阈值的心电QRS复合波检测算法[J]. 计算机应用, 2020, 40(1): 304-310.

WANG Zhizhong, LI Hongyi, HAN Chuang. QRS complex detection algorithm of electrocardiograph based on Shannon energy and adaptive threshold[J]. Journal of Computer Applications, 2020, 40(1): 304-310.

参考文献

[1] RAJ S, RAY K C, SHANKAR O. Development of robust, fast and efficient QRS complex detector:a methodological review[J]. Australasian Physical and Engineering Sciences in Medicine, 2018, 41(3):581-600.
[2] PAN J, TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Bio-Medical Engineering, 1985, BME-32(3):230-236.
[3] LU X, PAN M, YU Y. QRS detection based on improved adaptive threshold[J]. Journal of Healthcare Engineering, 2018, 2018:Article No. 5694595.
[4] SAHOO S, BISWAL P, DAS T, et al. De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding[J]. Procedia Technology, 2016, 25:68-75.
[5] NAKAI Y, IZUMI S, NAKANO M, et al. Noise tolerant QRS detection using template matching with short-term autocorrelation[C]//Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway:IEEE, 2014:34-37.
[6] SHIN J H, CHOI B H, LIM Y G, et al. Automatic ballistocardiogram (BCG) beat detection using a template matching approach[C]//Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway:IEEE, 2008:1144-1146.
[7] JENKAL W, LATIF R, TOUMANARI A, et al. Enhanced algorithm for QRS detection using discrete wavelet transform (DWT)[C]//Proceedings of the 27th International Conference on Microelectronics. Piscataway:IEEE, 2015:39-42.
[8] MARTÍNEZ J P, ALMEIDA R, OLMOS S, et al. A wavelet-based ECG delineator:evaluation on standard databases[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(4):570-581.
[9] 李明星,李旭雯,魏鑫.心电信号的QRS波检测算法的研究[J].兵器装备工程学报,2018,39(9):177-175.(LI M X, LI X W, WEI X. A study on QRS detection algorithm of ECG signal[J]. Journal of Ordnance Equipment Engineering, 2018, 39(9):177-175.
[10] BENITEZ D S, GAYDECKI P A, ZAIDI A, et al. A new QRS detection algorithm based on the Hilbert transform[C]//Proceedings of the Computers in Cardiology 2000. Piscataway:IEEE, 2000:379-382.
[11] FEDOTOV A A, AKULOVA A S, AKULOV S A. Effective QRS-detector based on Hilbert transform and adaptive thresholding[C]//Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, IFMBE 57. Cham:Springer, 2016:140-144.
[12] 李鸿强,苗长云,张龙宇,等.心电医疗监护物联网关键技术研究[J].计算机应用研究,2010,27(12):4600-4603.(LI H Q, MIAO C Y, ZHANG L Y, et al. Research on key technologies of ECG monitoring system based on Internet of things[J]. Application Research of Computers, 2010, 27(12):4600-4603.)
[13] D'ALOIA M, LONGO A, RIZZI M. Noisy ECG signal analysis for automatic peak detection[J]. Information, 2019, 10(2):Article No. 35.
[14] ŠARLIJA M, JURIŠIC F, POPOVIC S. A convolutional neural network based approach to QRS detection[C]//Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis. Piscataway:IEEE, 2017:121-125.
[15] PAL S, MITRA M. Empirical mode decomposition based ECG enhancement and QRS detection[J]. Computers in Biology and Medicine, 2012, 42(1):83-92.
[16] MANIKANDAN M S, SOMAN K P. A novel method for detecting R-peaks in ElectroCardioGram (ECG) signal[J]. Biomedical Signal Processing and Control, 2012, 7(2):118-128.
[17] BURGUERA A. Fast QRS detection and ECG compression based on signal structural analysis[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1):123-131.
[18] ELGENDI M, ESKOFIER B, DOKOS S, et al. Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems[J]. PLoS One, 2014, 9(1):Article No. e84018.
[19] LUO S, JOHNSTON P. A review of electrocardiogram filtering[J]. Journal of Electrocardiology, 2010, 43(6):486-496.
[20] THIAMCHOO N, PHUKPATTARANONT P. R peak detection algorithm based on continuous wavelet transform and Shannon energy[J]. ECTI Transactions on Computer and Information Technology, 2016, 10(2):167-175.
[21] MOODY G B, MARK R G. The impact of the MIT-BIH arrhythmia database[J]. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3):45-50.
[22] LAI D, ZHANG F, WANG C. A real-time QRS complex detection algorithm based on differential threshold method[C]//Proceedings of the 2015 IEEE International Conference on Digital Signal Processing. Piscataway:IEEE, 2015:129-133.
[23] SHARMA L D, SUNKARIA R K. A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency[J]. Measurement, 2016, 87:194-204.
[24] 孙亚楠,吕可嘉,张瑞.一种新的心电信号R峰自动检测方法[J].西北大学学报(自然科学版),2018,48(1):16-23.(SUN Y N, LYU K X, ZHANG R. A novel automatic R peak detection method using ECG[J]. Journal of Northwest University (Natural Science Edition), 2018, 48(1):16-23.)

基于香农能量与自适应阈值的心电QRS复合波检测算法

QRS complex detection algorithm of electrocardiograph based on Shannon energy and adaptive threshold

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王思鹏, 杜昌平, 叶志贤, 宋广华, 郑耀. 基于多步神经网络观测器的扑翼飞行器缓变故障检测[J]. 计算机应用, 2020, 40(8): 2449-2454.
[2]	胡雪影, 郭海儒, 朱蓉. 基于混合深度卷积网络的图像超分辨率重建[J]. 计算机应用, 2020, 40(7): 2069-2076.
[3]	张皓然, 王学渊, 李小霞. 基于自适应阈值活动语音检测和最小均方误差对数谱幅度估计的低信噪比降噪算法[J]. 计算机应用, 2020, 40(6): 1763-1768.
[4]	王忠锋, 徐志远, 宋纯贺, 张宏宇, 蔡颖凯. 基于梯度的深度网络剪枝算法[J]. 计算机应用, 2020, 40(5): 1253-1259.
[5]	傅洪亮, 雷沛之. 基于去噪自编码器和长短时记忆网络的语音测谎算法[J]. 计算机应用, 2020, 40(2): 589-594.
[6]	丁莲静, 刘光帅, 李旭瑞, 陈晓文. 加权信息熵与增强局部二值模式结合的人脸识别[J]. 计算机应用, 2019, 39(8): 2210-2216.
[7]	来杰, 王晓丹, 李睿, 赵振冲. 基于去噪自编码器的极限学习机[J]. 计算机应用, 2019, 39(6): 1619-1625.
[8]	郭旭东, 李小敏, 敬如雪, 高玉琢. 基于改进的稀疏去噪自编码器的入侵检测[J]. 计算机应用, 2019, 39(3): 769-773.
[9]	陶永鹏, 景雨, 顼聪. 基于分组字典与变分模型的图像去噪算法[J]. 计算机应用, 2019, 39(2): 551-555.
[10]	张雯雯, 韩裕生. 基于非局部自相似性的低秩稀疏图像去噪[J]. 计算机应用, 2018, 38(9): 2696-2700.
[11]	郭庆荣, 贾振红, 杨杰, Nikola KASABOV. 基于非下采样Shearlet变换与模糊对比度的合成孔径雷达图像增强[J]. 计算机应用, 2018, 38(9): 2701-2705.
[12]	尹立, 林新棋, 陈黎飞. 视频帧内运动目标移除篡改检测算法[J]. 计算机应用, 2018, 38(3): 879-883.
[13]	郭黎, 廖宇, 李敏, 袁海林, 李军. 自适应非局部数据保真项和双边总变分的图像去噪模型[J]. 计算机应用, 2017, 37(8): 2334-2342.
[14]	徐苏, 周颖玥. 基于图像分割的非局部均值去噪算法[J]. 计算机应用, 2017, 37(7): 2078-2083.
[15]	彭政, 陈东方, 王晓峰. 正则化超分辨率重建过程的自适应阈值去噪[J]. 计算机应用, 2017, 37(7): 2084-2088.