An online ECG QRS Detection Technique

A simple technique, based on measuring the amplitude-span and slope of QRS in Electrocardiograph (ECG) data is described in this paper. Detection of QRS is done in two phases, viz., training and detection. At first, the dataset is searched by sliding a window of 96 ms width, from which an amplitude and slope threshold criteria is learned. From these criteria a QRS template is defined in terms of some signatures. In the detection phase, a QRS is located by matching a QRS template in a sliding 96 ms window. MATLAB simulation, using proposed technique with ECG data from Physionet yields over 98% accuracy. The algorithm is implemented on a standalone embedded system using 8051 microcontroller, which processes 10-12 beats stored in the external on-board RAM. The technique is suitable for online computation of heart rate.

[1]  W J Tompkins,et al.  Applications of artificial neural networks for ECG signal detection and classification. , 1993, Journal of electrocardiology.

[2]  R. Orglmeister,et al.  QRS Detection Using Zero Crossing Counts , 2003 .

[3]  H.-Y. Zhou,et al.  Embedded real-time QRS detection algorithm for pervasive cardiac care system , 2008, 2008 9th International Conference on Signal Processing.

[4]  Sarabjeet Singh Mehta,et al.  Identification of QRS complexes in 12-lead electrocardiogram , 2009, Expert Syst. Appl..

[5]  Ying Liu,et al.  Adaptive Threshold for QRS Complex Detection Based on Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[6]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[7]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

[8]  P.E. Trahanias,et al.  An approach to QRS complex detection using mathematical morphology , 1993, IEEE Transactions on Biomedical Engineering.

[9]  M. R. Neuman,et al.  QRS wave detection , 2006, Medical and Biological Engineering and Computing.

[10]  Ashish Shukla,et al.  A fast and accurate FPGA based QRS detection system , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Zhang Xu,et al.  Accurate and Rapid QRS Detection for Intelligent ECG Monitor , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[12]  Mak Peng Un,et al.  QRS Recognition with Programmable Hardware , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[13]  Zhen Ji,et al.  Adaptive Lifting Scheme for ECG QRS complexes detection and its FPGA implementation , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[14]  Fei Zhang,et al.  An Effective QRS Detection Algorithm for Wearable ECG in Body Area Network , 2007, 2007 IEEE Biomedical Circuits and Systems Conference.

[15]  D.S. Benitez,et al.  A new QRS detection algorithm based on the Hilbert transform , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[16]  Zhongwei Jiang,et al.  Development of QRS detection algorithm designed for wearable cardiorespiratory system , 2009, Comput. Methods Programs Biomed..

[17]  Masahiko Okada,et al.  A Digital Filter for the ORS Complex Detection , 1979, IEEE Transactions on Biomedical Engineering.

[18]  Gerard Olivar,et al.  FPGA-Based Implementation of an Adaptive Canceller for 50/60-Hz Interference in Electrocardiography , 2007, IEEE Transactions on Instrumentation and Measurement.

[19]  S. Cerutti,et al.  ECG fiducial points detection through wavelet transform , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[20]  G.G. Cano,et al.  An approach to cardiac arrhythmia analysis using hidden Markov models , 1990, IEEE Transactions on Biomedical Engineering.

[21]  Willis J. Tompkins,et al.  Automated High-Speed Analysis of Holter Tapes with Microcomputers , 1983, IEEE Transactions on Biomedical Engineering.

[22]  Willis J. Tompkins,et al.  Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database , 1986, IEEE Transactions on Biomedical Engineering.