Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
A Combinatorial Model for ECG Interpretation

Authors: Costas S. Iliopoulos, Spiros Michalakopoulos


A new, combinatorial model for analyzing and inter- preting an electrocardiogram (ECG) is presented. An application of the model is QRS peak detection. This is demonstrated with an online algorithm, which is shown to be space as well as time efficient. Experimental results on the MIT-BIH Arrhythmia database show that this novel approach is promising. Further uses for this approach are discussed, such as taking advantage of its small memory requirements and interpreting large amounts of pre-recorded ECG data.

Keywords: Combinatorics, ECG analysis, MIT-BIH Arrhythmia Database, QRS Detection, String Algorithms

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1498


[1] Mary B. Conover. Understanding Electrocardiography. Mosby, Novem- ber 2002.
[2] Springhouse (Editor). ECG Interpretation Made Incredibly Easy! Lippincott Williams & Wilkins, 4th edition, 2007.
[3] Emanuel Stein. Rapid Analysis of Arrhythmias. Lippincott Williams & Wilkins, 3rd edition, 2000.
[4] Malcolm S. Thaler. The Only EKG Book You-ll Ever Need. Lippincott Williams & Wilkins, 5th edition, October 2006.
[5] Massachusets Institute of Technology. Mit-bih ecg database. Available:
[6] G.B. Moody and R.G. Mark. Development and evaluation of a 2-lead ecg analysis program. Computers in Cardiology, pages 39-44, 1982.
[7] Yu Hen Hu, S. Palreddy, and W. J. Tompkins. A patient-adaptable ecg beat classifier using a mixture of experts approach. Biomedical Engineering, IEEE Transactions on, 44(9):891-900, 1997.
[8] V. X. Afonso, O. Wieben, W. J. Tompkins, T. Q. Nguyen, and Shen Luo. Filter bank-based ecg beat classification. In Proceedings of the 19th Annual International Conference of the IEEE, volume 1, pages 97-100. Engineering in Medicine and Biology Society, October 1997.
[9] B. U. Kohler, C. Hennig, and R. Orglmeister. The principles of software qrs detection. Engineering in Medicine and Biology Magazine, 21(1):42- 57, 2002.
[10] Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso. Applications of artificial neural networks for ecg signal detection and classification. Journal of Electrocardiology, 26, 1994.
[11] V. X. Afonso, W. J. Tompkins, T. Q. Nguyen, and Shen Luo. Ecg beat detection using filter banks. Biomedical Engineering, IEEE Transactions on, 46(2):192-202, Feb. 1999.
[12] Hisashi Inoue and Akio Miyazaki. A noise reduction method for ecg signals using the dyadic wavelet transform (special section of papers selected from itc-cscc-97). IEICE transactions on fundamentals of electronics, communications and computer sciences, 81(6):1001-1007, 1998.
[13] J. K. Udupa and I. S. N. Murthy. Syntactic approach to ecg rhythm analysis. IEEE Trans. Biomed. Eng., BME-27(7):370- 375, July 1980.
[14] G. Papakonstantinou, E. Skordalakis, and F. Gritzali. An attribute grammar for qrs detection. Pattern Recogn., 19(4):297-303, 1986.
[15] Dan Gusfield. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press, 1997.
[16] Pavel A. Pevzner. Computational Molecular Biology: An Algorithmic Approach. The MIT Press, August 2000.
[17] Philip De Chazal, Maria O-Dwyer, and Richard B. Reilly. Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51:1196-1206, 2004.
[18] ANSI/AAMI. EC38: Ambulatory Electrocardiographs. Association for the Advancement of Medical Instrumentation, 1998.
[19] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000 (June 13). Circulation Electronic Pages:
[20] F. Sufi, Q. Fang, and I. Cosic. Ecg r-r peak detection on mobile phones. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pages 3697-3700, 2007.
[21] X. Chen, C. T. Ho, E. T. Lim, and T. Z. Kyaw. Cellular phone based online ecg processing for ambulatory and continuous detection. Computers in Cardiology, pages 653-656, Sept. 2007.
[22] J. A. Crowe, N. M. Gibson, M. S.Woolfson, and M. G. Somekh. Wavelet transform as a potential tool for ecg analysis and compression. Biomed. Eng., 14(3), 1992.
[23] Michael L. Hilton. Wavelet and wavelet packet compression of electro- cardiograms. IEEE Trans. Biomed. Eng, 44:394-402, 1997.
[24] ANSI/AAMI. EC57: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment. Association for the Advancement of Medical Instrumentation, 1998.
[25] George B. Moody. WFDB Applications Guide. Harvard-MIT Division of Health Sciences and Technology, 10th edition, November 2008.
[26] Maxime Crochemore, Christophe Hancart, and Thierry Lecroq. Algo- rithms on Strings. Cambridge University Press, 2007.
[27] Alfred V. Aho and Margaret J. Corasick. Efficient string matching: an aid to bibliographic search. Commun. ACM, 18(6):333-340, 1975.