基于局部方差的QRS波群和R峰检测算法

赵恺; 晏炜宏; 李永福

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基于局部方差的QRS波群和R峰检测算法

更新时间：2026-09-04

- 基于局部方差的QRS波群和R峰检测算法
- An accurate QRS detection and R peak location algorithm based on local derivative
- 微电子学与计算机 2021年38卷第2期页码：1-6
- 作者机构：
  
  上海交通大学微纳电子学系,上海,200240
- 作者简介：
- 基金信息：
  
  科技部重点研发计划 2019YFB2204500上海市科委“科技创新行动计划”科学仪器领域项目 1914220370
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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赵恺, 晏炜宏, 李永福. 基于局部方差的QRS波群和R峰检测算法[J]. 微电子学与计算机, 2021, 38(2): 1-6.

ZHAO Kai, YAN Wei-hong, LI Yong-fu. An accurate QRS detection and R peak location algorithm based on local derivative[J]. 2021, 38(2): 1-6.
赵恺, 晏炜宏, 李永福. 基于局部方差的QRS波群和R峰检测算法[J]. 微电子学与计算机, 2021, 38(2): 1-6. DOI：

ZHAO Kai, YAN Wei-hong, LI Yong-fu. An accurate QRS detection and R peak location algorithm based on local derivative[J]. 2021, 38(2): 1-6. DOI：

摘要

在与心电图相关的前沿应用中，需要获得心电图中QRS波群和R峰的准确位置，但现有的QRS波群检测算法在干扰条件下准确度不足.针对这个问题，提出了一种能准确检测QRS波群和R峰的方法.该方法先使用一种基于局部方差的非线性变换对心电图中的QRS波群进行增强，然后基于已被检测到的QRS波群的信息来设置动态阈值找出新的QRS波群，最后，一种准确的R峰识别方法被用来找到R峰的位置.相对于现有的算法，该算法在多个开源心电数据库上达到了较高的性能，并对QT数据库达到99.98%的灵敏度，99.94%的真阳率，和0.08%的误检率.

Abstract

Advanced electrocardiogram analytic applications

such as heart rate variability analysis

generally require accurate detection of R peaks and QRS complexes

simultaneously

but the existing methods are not accurate enough under interference. To address the problem

we propose a method to detect the accurate positions of QRS complexes and R peaks. The proposed algorithm begins with a non-linear transformation to enhance QRS complexes in electrocardiogram signal

and dynamic thresholds are calculated based on the detected QRS complexes to detect the new QRS complexes

and finally

an accurate R-peak identifier function is used to locate R peaks. Compared with the existing algorithms

the proposed algorithm achieves a better performance on several open-source electrocardiogram databases. It achieves 99.98% sensitivity

99.94% positive predictivity and 0.08% detection error rate on QT Database.

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Keywords

references

PANDIT D, ZHANG L, LIU C Y, et al. A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm[J]. Computer Methods and Programs in Biomedicine, 2017, 144: 61-75. DOI:10.1016/j.cmpb.2017.02.028.

PAN J P, TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Biomedical Engineering, 1985, BME-32(3): 230-236. DOI:10.1109/TBME.1985.325532.

KLEIGER R E, MILLER J P, BIGGER JR J T, et al. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction[J]. The American Journal of Cardiology, 1987, 59(4): 256-262. DOI:10.1016/0002-9149(87)90795-8.

IYENGAR N, PENG C K, MORIN R, et al. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics[J]. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 1996, 271(4): R1078-R1084. DOI:10.1152/ajpregu.1996.271.4. R1078.

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. DOI:10.1109/51.932724.

LAGUNA P, MARK R G, GOLDBERG A, et al. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG[C]//Proceedings of Computers in Cardiology1997. Lund, Sweden: IEEE, 1997: 673-676. DOI: 10.1109/CIC.1997.648140.

GOLDBERGER A L, AMARAL L A N, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): e215-e220. DOI:10.1161/01.CIR.101.23. e215.

THONG T, MCNAMES J, ABOY M, et al. Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(4): 561-569. DOI:10.1109/TBME.2003.821030.

GREENWALD S D, PATIL R S, MARK R G. Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information[C]//Proceedings of 1990Computers in Cardiology. Chicago, IL, USA: IEEE, 1990: 461-464. DOI: 10.1109/CIC.1990.144257.

MOODY G B. The physionet/computers in cardiology challenge 2008: T-wave alternans[C]//Proceedings of 2008 Computers in Cardiology. Bologna, Italy: IEEE, 2008: 505-508. DOI: 10.1109/CIC.2008.4749089.

KARIMIAN N, GUO Z M, TEHRANIPOOR M, et al. Highly reliable key generation from electrocardiogram (ECG)[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(6): 1400-1411. DOI:10.1109/TBME.2016.2607020.

DE CHAZAL P, O'DWYER M, REILLY R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(7): 1196-1206. DOI:10.1109/TBME.2004.827359.

MOODY G B, MULDROW W E, MARK R G. A noise stress test for arrhythmia detectors[J]. Computers in Cardiology, 1984, 11: 381-384. DOI: 381

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