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.
[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:
http://ecg.mit.edu/.
[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:
http://circ.ahajournals.org/cgi/content/full/101/23/e215.
[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.
[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:
http://ecg.mit.edu/.
[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:
http://circ.ahajournals.org/cgi/content/full/101/23/e215.
[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.
@article{"International Journal of Information, Control and Computer Sciences:60839", author = "Costas S. Iliopoulos and Spiros Michalakopoulos", title = "A Combinatorial Model for ECG Interpretation", abstract = "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", volume = "3", number = "6", pages = "1621-5", }
