Comparison of characterizing and data analysis methods for detecting abnormalities in ECG

The electrocardiogram (ECG) is an important bioelectrical signal which gives valuable information about the functional aspects of the heart both in normal and abnormal conditions. For several decades a considerable amount of research activity has been directed towards advancement in the clinical diagnosing of this disease using surface ECG and symptoms. This paper compares different methods that have evolved over the years for elimination of different types of noise present in ECG and for characterizing ECG for the detection of myocardial ischemia and infarction which is a life threatening heart disease. The first part of the review gives broad overview of the research undergone in the area of denoising methods deployed and second part describes different types of ischemia and methods to detect the disease using ECG.

[1]  H. Wellens,et al.  The ECG in Acute Myocardial Infarction and Unstable Angina , 2002, Developments in Cardiovascular Medicine.

[2]  J. S. Sahambi,et al.  Using Wavelet Transforms for ECG Characterization , 1997 .

[3]  Jacek M. Leski,et al.  ECG baseline wander and powerline interference reduction using nonlinear filter bank , 2005, Signal Process..

[4]  Allan Kardec Barros,et al.  independent , 2006, Gumbo Ya Ya.

[5]  Fayyaz A. Afsar,et al.  A Comparison of Baseline Removal Algorithms for Electrocardiogram (ECG) Based Automated Diagnosis of Coronory Heart Disease , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[6]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[7]  Ivan Dotsinsky,et al.  Power-line interference elimination from ECG in case of non-multiplicity between the sampling rate and the power-line frequency , 2008, Biomed. Signal Process. Control..

[8]  H BLACKBURN,et al.  THE EXERCISE ELECTROCARDIOGRAM; EXPERIENCE IN ANALYSIS OF "NOISY" CARDIOGRAMS WITH A SMALL COMPUTER. , 1965, American heart journal.

[9]  S.N. Tandon,et al.  Using wavelet transforms for ECG characterization. An on-line digital signal processing system , 1997, IEEE Engineering in Medicine and Biology Magazine.

[10]  James F. Kaiser,et al.  The use of a masking signal to improve empirical mode decomposition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[11]  Kee-Ho Yu,et al.  Development of ambulatory ECG monitoring device with ST shape classification , 2007, 2007 International Conference on Control, Automation and Systems.

[12]  H. G. Goovaerts,et al.  Real-time method for noninvasive recording of His bundle activity of the electrocardiogram. , 1976, Computers and biomedical research, an international journal.

[13]  Zhi-Dong Zhao,et al.  A New Method for Removal of Baseline Wander and Power Line Interference in ECG Signals , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[14]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[15]  Kee-Ho Yu,et al.  Design of Ambulatory ECG Monitoring System to detect ST pattern change , 2006, 2006 SICE-ICASE International Joint Conference.

[16]  S. Hohnloser,et al.  Assessment of QT dispersion for prediction of mortality or arrhythmic events after myocardial infarction: results of a prospective, long-term follow-up study. , 1998, Circulation.

[17]  Lionel Tarassenko,et al.  Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.

[18]  M. Merri,et al.  Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability , 1988, IEEE Transactions on Biomedical Engineering.

[19]  Li Su,et al.  De-Noising of ECG Signal Using Translation- Invariant Wavelet De-Noising Method with Improved Thresholding , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[20]  Donghui Zhang,et al.  Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[21]  A. D. Jose,et al.  Effect of combined sympathetic and parasympathetic blockade on heart rate and cardiac function in man. , 1966, The American journal of cardiology.

[22]  Pablo Laguna,et al.  Discrimination between ischemic and artifactual ST segment events in Holter recordings , 2010, Biomed. Signal Process. Control..

[23]  Alberto Herreros,et al.  Exploration of Supraventricular Conduction with respect to Atrial Fibrillation. Methodological Aspects on Selected Techniques , 2005 .

[24]  Pablo Laguna,et al.  Multilead ECG Delineation Using Spatially Projected Leads From Wavelet Transform Loops , 2009, IEEE Transactions on Biomedical Engineering.

[25]  Willis J. Tompkins,et al.  Biomedical Digital Signal Processing , 1993 .

[26]  Pablo Laguna,et al.  QT variability and HRV interactions in ECG: quantification and reliability , 2006, IEEE Transactions on Biomedical Engineering.

[27]  J. Couderc.  Cardiac regulation and electrocardiographic factors contributing to the measurement of repolarization variability. , 2009, Journal of electrocardiology.

[28]  M. J. English,et al.  Signal processing for recovery of cardiac conducting system activity , 1982 .

[29]  Mang I Vai,et al.  Beat-to-beat ECG ventricular late potentials variance detection by filter bank and wavelet transform as beat-sequence filter , 2004, IEEE Transactions on Biomedical Engineering.

[30]  P. Caminal,et al.  Alignment methods for averaging of high-resolution cardiac signals: a comparative study of performance , 1991, IEEE Transactions on Biomedical Engineering.

[31]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[32]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[33]  Anastasios Bezerianos,et al.  A theoretical analysis of acute ischemia and infarction using ECG reconstruction on a 2-D model of myocardium , 2001, IEEE Transactions on Biomedical Engineering.

[34]  Hannes Mühlthaler,et al.  A training whole-heart model for simulating propagation and ECG patterns , 2007, Biomed. Signal Process. Control..

[35]  A.J. Nimunkar,et al.  EMD-based 60-Hz noise filtering of the ECG , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  Philip Langley,et al.  Effect of changes in heart rate and in action potential duration on the electrocardiogram T wave shape. , 2002, Physiological measurement.

[37]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[38]  H N Keiser,et al.  Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. , 1977, Computers and biomedical research, an international journal.

[39]  Elin Trägårdh,et al.  Reduced high-frequency QRS components in electrocardiogram leads facing an area of the heart with intraventricular conduction delay due to bundle branch block. , 2007, Journal of electrocardiology.

[40]  J. van Alsté,et al.  Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps , 1985, IEEE Transactions on Biomedical Engineering.

[41]  O Pahlm,et al.  Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion. , 2000, Journal of the American College of Cardiology.

[42]  Alan H Feiveson,et al.  Real-time 12-lead high-frequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. , 2004, Mayo Clinic proceedings.

[43]  M. Merri,et al.  Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability , 1990 .

[44]  Yunfeng Wu,et al.  An Unbiased Linear Artificial Neural Network with Normalized Adaptive Coefficients for Filtering Noisy ECG Signals , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[45]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: A Case-Study Approach , 2001 .

[46]  Jean-Marc Vesin,et al.  Cancellation of Ventricular Activity in the ECG: Evaluation of Novel and Existing Methods , 2007, IEEE Transactions on Biomedical Engineering.

[47]  P. Oldershaw,et al.  Relation of human cardiac action potential duration to the interval between beats: implications for the validity of rate corrected QT interval (QTc). , 1987, British heart journal.

[48]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[49]  D. J. Woollons,et al.  Noninvasive recording of electrical activity in the PR segment in man. , 1978, British heart journal.

[50]  P. Caminal,et al.  Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques , 1992, IEEE Transactions on Biomedical Engineering.

[51]  Pablo Laguna,et al.  Adaptive filtering of ECG baseline wander , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[52]  G.B. Moody,et al.  PhysioNet: a Web-based resource for the study of physiologic signals , 2001, IEEE Engineering in Medicine and Biology Magazine.

[53]  S. V. Pandit.  ECG baseline drift removal through STFT , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[54]  E. Ashley,et al.  An evidence-based review of the resting electrocardiogram as a screening technique for heart disease. , 2001, Progress in cardiovascular diseases.

[55]  Senén Barro,et al.  A new approach for TU complex characterization , 2000, IEEE Transactions on Biomedical Engineering.

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

[57]  A. C. Hunt.  BMC Cardiovascular Disorders BioMed Central Research article BMC 22002, Cardiovascular Disorders x T Wave Alternans in high arrhythmic risk patients: Analysis in time and frequency domains: A pilot study , 2002 .