Detection of QRS Complexes in 12-lead ECG using Adaptive Quantized Threshold

†† Summary The QRS complex is the most prominent wave component within the electrocardiogram. It reflects the electrical activity of heart during the ventricular contraction and the time of its occurrence. Its morphology provides information about the current state of the heart. The identification of QRS-complexes forms the basis for almost all automated ECG analysis algorithms. The presented algorithm employs a modified definition of slope, of ECG signal, as the feature for detection of QRS. A sequence of transformations of the filtered and baseline drift corrected ECG signal is used for extraction of a new modified slope-feature. Two feature-components are combined to derive the final QRSfeature signal. Multiple quantized amplitude thresholds are employed for distinguishing QRS-complexes from non-QRS regions of the ECG waveform. An adequate amplitude threshold is automatically selected by the presented algorithm and is utilized for delineating the QRS-complexes. A QRS detection rate of 98.56% with false positive and false negative percentage of 0.82% and 1.44% has been reported.

[1]  R. Poli,et al.  Genetic design of optimum linear and nonlinear QRS detectors , 1995, IEEE Transactions on Biomedical Engineering.

[2]  L. Sörnmo,et al.  Delineation of the QRS complex using the envelope of the e.c.g. , 1983, Medical and Biological Engineering and Computing.

[3]  M. L. Ahlstrom,et al.  Digital Filters for Real-Time ECG Signal Processing Using Microprocessors , 1985, IEEE Transactions on Biomedical Engineering.

[4]  N. S. Lingayat,et al.  ECG Pattern Classification Using Support Vector Machine , 2006 .

[5]  J L Willems,et al.  Common standards for quantitative electrocardiography. , 1985, Journal of medical engineering & technology.

[6]  Y Sun,et al.  Microcontroller-based real-time QRS detection. , 1992, Biomedical instrumentation & technology.

[7]  Willis J. Tompkins,et al.  Adaptive matched filtering for QRS detection , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Yao-Nan Wang,et al.  A method to choose kernel function and its parameters for support vector machines , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[9]  F. Gritzali.  Towards a generalized scheme for QRS detection in ECG waveforms , 1988 .

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

[11]  S. C. Saxena,et al.  Computer-aided interpretation of ECG for diagnostics , 1996, Int. J. Syst. Sci..

[12]  K. Egiazarian,et al.  Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. , 2006, Medical engineering & physics.

[13]  W.J. Tompkins,et al.  Neural-network-based adaptive matched filtering for QRS detection , 1992, IEEE Transactions on Biomedical Engineering.

[14]  A. Robert LeBlanc,et al.  Near-Optimal Signal Preprocessor for Positive Cardiac Arrhythmia Identification , 1985, IEEE Transactions on Biomedical Engineering.

[15]  U. Niranjan,et al.  Component wave delineation of ECG by filtering in the Fourier domain , 1992, Medical and Biological Engineering and Computing.

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

[17]  H. K. Verma,et al.  ANN-based QRS-complex analysis of ECG. , 1998, Journal of medical engineering & technology.

[18]  Stanislaw Osowski,et al.  Support vector machine-based expert system for reliable heartbeat recognition , 2004, IEEE Transactions on Biomedical Engineering.

[19]  Yukinori Suzuki.  Self-organizing QRS-wave recognition in ECG using neural networks , 1995, IEEE Trans. Neural Networks.

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

[21]  Sarabjeet Singh Mehta,et al.  Total Removal of Baseline Drift from ECG Signal , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[22]  A Ligtenberg,et al.  A robust-digital QRS-detection algorithm for arrhythmia monitoring. , 1983, Computers and biomedical research, an international journal.

[23]  Ashutosh,et al.  Evolutionary Selection of Kernels in Support Vector Machines , 2006, 2006 International Conference on Advanced Computing and Communications.

[24]  I.S.N. Murthy,et al.  Analysis of ECG from pole-zero models , 1992, IEEE Transactions on Biomedical Engineering.

[25]  S. Suppappola,et al.  Nonlinear transforms of ECG signals for digital QRS detection: a quantitative analysis , 1994, IEEE Transactions on Biomedical Engineering.

[26]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.