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Fast ECG baseline wander removal preserving the ST segment

Published: 26 October 2011 Publication History

Abstract

Baseline wander removal is an unavoidable preprocessing step in ECG signal analysis. Unfortunately, the in-band nature of this kind of noise makes its removal difficult without affecting ECG waveform, in particular the ST segment. This is a portion of the ECG with high clinical relevance, as it is related to the diagnosis of acute coronary syndromes. The ST segment is highly susceptible to distortion when baseline removal is performed affecting the low-frequency region of ECG spectrum, where are concentrated the harmonic components that mainly contribute to the shape of the ST segment. In this paper, we propose to tackle the problem of baseline removal from a different perspective, considering the quadratic variation as an alternative measure of variability not directly related to the frequency domain. In this regard, we recently proposed a novel baseline removal algorithm based on quadratic variation reduction. In this paper, we assess its performance with respect to the distortion of the ST segment comparing it to state-of-the-art algorithms. Simulation results confirm the effectiveness of the approach based on quadratic variation reduction. Our algorithm outperforms state-of-the-art algorithms tailored to minimize distortion of the ST segment. Moreover, it compares favorably also in terms of computational complexity, which is linear in the size of the vector to detrend. This makes it suitable also for real-time applications.

References

[1]
2005 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care, Part 8: Stabilization of the Patient With Acute Coronary Syndromes. Circulation, 112(24 suppl):IV89--IV110, 2005.
[2]
S. Achar, S. Kundu, and W. Norcross. Diagnosis of acute coronary syndrome. American Family Physician, 72(1):119--126, 2005.
[3]
F. Afsar, M. Riaz, and M. Arif. A comparison of baseline removal algorithms for electrocardiogram (ECG) based automated diagnosis of coronory heart disease. Bioinformatics and Biomedical Engineering, 2009, ICBBE 2009, 3rd International Conference on, 2009.
[4]
J. J. Bailey. The triangular wave test for electrocardiographic devices: A historical perspective. Journal of Electrocardiology, 37(Supplement 1):71--73, 2004.
[5]
M. Blanco-Velasco, B. Wengb, and K. Barnerc. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, 38:1--13, 2008.
[6]
S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, March 2004.
[7]
V. Chouhan and S. Mehta. Total Removal of Baseline Drift from ECG Signal. Int. Conf. on Computing: Theory and Applications 2007 (ICCTA07), 2007.
[8]
C. Chu and E. Delp. Impulsive Noise Suppression and Background Normalization of Electrocardiogram Signals Using Morphological Operators. IEEE Trans. Biomed. Eng., 36(2):262--273, 1989.
[9]
G. D. Clifford, F. Azuaje, and P. McSharry. Advanced Methods and Tools for ECG Data Analysis. Artech House, Inc., Norwood, MA, USA, 2006.
[10]
A. Fasano, V. Villani, and L. Vollero. Baseline wander estimation and removal by quadratic variation reduction. Engineering in Medicine and Biology Society, 2011 33rd Annual International Conference of the IEEE, 2011.
[11]
R. Frankel, E. Pottala, R. Bowser, and J. Bailey. A Filter to Suppress ECG Baseline Wander and Preserve ST-Segment Accuracy in a Real-time Environment. Journal of Electrocardiology, 24(4):315--323, October 1991.
[12]
A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. K. Peng, and H. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23):e215--e220, 2000.
[13]
A. Guyton and J. Hall. Textbook of Medical Physiology. Elsevier Saunders, 11th edition, 2006.
[14]
S. Hargittai. Efficient and fast ECG baseline wander reduction without distortion of important clinical information. Computers in Cardiology, 35:841--844, 2008.
[15]
R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, February 1990.
[16]
A. R. Houghton and D. Gary. Making Sense of the ECG: A Hands-on Guide. Arnold Publishing Co., 2003.
[17]
P. J. Huber and E. M. Ronchetti. Robust Statistics. Wiley, 2009.
[18]
P. Kligfield, L. Gettes, J. Bailey, R. Childers, B. Deal, E. Hancock, G. van Herpen, J. Kors, P. Macfarlane, D. Mirvis, O. Pahlm, P. Rautaharju, and G. Wagner. Recommendations for the standardization and interpretation of the electrocardiogram: part I: The electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation, 115(10):1306--1324, 2007.
[19]
P. Laguna, R. Jane, and P. Caminal. Adaptive Filtering of ECG Baseline Wander. Proc. 14th Annu. Int. Conf. IEEE Eng. Medicine Biol. Soc. 1992 (EMBS 1992), pages 508--509, 1992.
[20]
C. R. Meyer and H. N. Keiser. Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Computers and Biomedical Research, 10(5):459--470, Oct. 1977.
[21]
C. Papaloukas, D. Fotiadis, A. Likas, A. Liavas, and L. Michalis. A Knowledge-based Technique for the Detection of Ischemic Episodes in Long Duration ECGs. Medical and Biological Engineering and Computing, 39:105--112, 2001.
[22]
S. Papapietro, G. Niess, T. Paine, J. Mantle, C. Rackley, R. R. Jr., and W. Rogers. Transient electrocardiographic changes in patients with unstable angina: Relation to coronary arterial anatomy. The American Journal of Cardiology, 46(1):28--33, 1980.
[23]
K. Park, K. Lee, and H. Yoon. Application of a wavelet adaptive filter to minimise distorion of the ST-segment. Medical and Biological Engineering and Computing, 36(5):581--586, September 1998.
[24]
M. Sedaaghi. An Efficient ECG Background Normalization. 13th European Signal Processing Conference, 2005.
[25]
L. T. Sheffield, C. A. Berson, D. B. Remschel, P. C. Gillette, R. E. Hermes, L. Hinkle, H. Kennedy, D. M. Mirvis, and C. Oliver. Recommendations for standard of instrumentation and practice in the use of ambulatory electrocardiography. Circulation, pages 626A--636A, 1985.
[26]
S. E. Shreve. Stochastic Calculus for Finance II: Continuous-Time Models. Springer Science+Business Media, Inc., 2004.
[27]
V. Shusterman, S. Shah, A. Beigel, and K. Anderson. Enhancing the precision of ECG baseline correction: Selective filtering and removal of residual error. Computers and Biomedical Research, 33:144--160, 2000.
[28]
P. Sun, Q. H. Wu, A. M. Weindling, A. Finkelstein, and K. Ibrahim. An improved morphological approach to background normalization of ECG signals. IEEE Trans. Biomed. Eng., 50(1):117--121, 2003.
[29]
A. Taddei, G. Distante, M. Emdin, P. Pisani, G. Moody, C. Zeelenberg, and C. Marchesi. The European ST-T Database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European Heart Journal, 13:1164--1172, 1992.
[30]
K. Thygesen, J. Alpert, H. White, and Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal Definition of Myocardial Infarction. Journal of the American College of Cardiology, 50(22):2173--2195, 2007.
[31]
J. A. Van Alste and T. S. Schilder. Removal of base-line wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans. Biomed. Eng., 32(12):1052--1060, Dec 1985.
[32]
G. Zheng, Y. Gu, and M. Dai. Distortion measurement of ECG baseline wander elimination. Journal of Computational Information Systems, 7(5):1770--1777, 2011.

Cited By

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  • (2019)Baseline wander removal for bioelectrical signals by quadratic variation reductionSignal Processing10.1016/j.sigpro.2013.11.03399(48-57)Online publication date: 3-Jan-2019
  • (2016)Adaptive quadratic regularization for baseline wandering removal in wearable ECG devices2016 24th European Signal Processing Conference (EUSIPCO)10.1109/EUSIPCO.2016.7760542(1718-1722)Online publication date: Aug-2016
  • (2016)Regularized LMS methods for baseline wandering removal in wearable ECG devices2016 IEEE 55th Conference on Decision and Control (CDC)10.1109/CDC.2016.7799038(5029-5034)Online publication date: Dec-2016
  • Show More Cited By

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cover image ACM Other conferences
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
October 2011
949 pages
ISBN:9781450309134
DOI:10.1145/2093698
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Universitat Pompeu Fabra
  • IEEE
  • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
  • River Publishers: River Publishers
  • CTTC: Technological Center for Telecommunications of Catalonia
  • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2011

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Author Tags

  1. ECG
  2. ST segment
  3. baseline wander
  4. quadratic variation

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ISABEL '11
Sponsor:
  • Technical University of Catalonia Spain
  • River Publishers
  • CTTC
  • CTIF

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Cited By

View all
  • (2019)Baseline wander removal for bioelectrical signals by quadratic variation reductionSignal Processing10.1016/j.sigpro.2013.11.03399(48-57)Online publication date: 3-Jan-2019
  • (2016)Adaptive quadratic regularization for baseline wandering removal in wearable ECG devices2016 24th European Signal Processing Conference (EUSIPCO)10.1109/EUSIPCO.2016.7760542(1718-1722)Online publication date: Aug-2016
  • (2016)Regularized LMS methods for baseline wandering removal in wearable ECG devices2016 IEEE 55th Conference on Decision and Control (CDC)10.1109/CDC.2016.7799038(5029-5034)Online publication date: Dec-2016
  • (2015)An Experimental Investigation of Wavelets for ECG Signal DenoisingProceedings of the The International Conference on Engineering & MIS 201510.1145/2832987.2833033(1-7)Online publication date: 24-Sep-2015
  • (2014)ECG baseline wander removal by QVR preserving the ST segment2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)10.1109/ESGCO.2014.6847547(117-118)Online publication date: May-2014

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