Identification of Cardiac Arrhythmias using Natural Resonance Complex Frequencies
An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
[1] World Health organization," Cardiovascular diseases", Fact sheet
N┬░317, February 2007. http://www.who.int/cardiovascular_diseases.
[2] Rajendra Acharya U, Jasjit S. Suri, Jos A.E. Spaan, S .M. Krishnan,
"Advances in Cardiac Signal Processing", ISBN-13 978-3-540-36674-4
Springer Berlin Heidelberg New York, 2007.
[3] SW Chen. "Two-stage discrimination of cardiac arrhythmias using a
total least squares-based prony modeling algorithm" IEEE Transaction
on Biomedical Engineering, 47: pp. 1317-1326, 2000.
[4] Owis, M., Abou-Zied, A., Youssef, A.B., Kadah, Y., "Robust feature
extraction from ECG signals based on nonlinear dynamical modeling,"
23rd Annual International Conference IEEE Engineering in Medicine
and Biology Society. (EMBC-01). Volume 2. pp. 1585-1588, 2001.
[5] Dingfei Ge, Narayanan Srinivasan, Shankar Krishnan. "Cardiac
arrhythmia classification using autoregressive modeling" BioMedical
Engineering OnLine, 1(1):5, pp. 1585-1588, 2002.
[6] GE Ding-Fei, HOU Bei-Ping, and XIANG Xin-Jian, "Study of Feature
Extraction Based on Autoregressive Modeling in ECG Automatic
Diagnosis", ACTA Automation Sinica. Vol. 33 No. 5. pp. 462-466,
2007.
[7] P. de Chazal, M. O-Dwyer, and R. B. Reilly, "Automatic Classification
of Heartbeats Using ECG Morphology and Heartbeat Interval Features,"
IEEE Transaction on Biomedical Engineering, Vol. 51, No. 7, pp.1196-
1206, July 2004.
[8] Inan, O.T., Giovangrandi, L. and Kovacs, G.T.A., "Robust neuralnetwork-
based classification of premature ventricular contractions using
wavelet transform and timing interval features", IEEE Transaction on
Biomedical Engineering, Vol. 53, No.12. pp. 2507-2515, 2006.
[9] Ahmad R. Naghsh-Nilchi and A. Rahim Kadkhoda mohammadi,
"Cardiac Arrhythmias Classification Method Based on MUSIC,
Morphological Descriptors, and Neural Network", EURASIP Journal on
Advances in Signal Processing, Article No. 202. Volume 2008.
[10] Turker Ince, S. Kiranyaz, and M. Gabbouj, "A Generic and Robust
System for Automated Patient-specific Classification of
Electrocardiogram Signals", IEEE Transactions on Biomedical
Engineering, Vol. 56, No. 5, May 2009.
[11] S. S. Mehta, Nitin Shivappa Lingayat, "Support Vector Machine for
Cardiac Beat Detection in Single Lead Electrocardiogram". IAENG
International Journal of Applied Mathematics, pp. 1630-1635, 2007.
[12] B. Mohammad zadeh Asl, S.K. Setarehdan, "Neural Network Based
Arrhythmia Classification Using Heart Rate Variability Signal",
Proceedings of the 2nd International Symposium on Biomedical
Engineering, Bangkok, Thailand, Nov. 2006.
[13] B. Anuradha and V. C. Veera Reddy, "ANN for classification of cardiac
arrhythmias", ARPN Journal of Engineering and Applied Sciences, Vol.
3, No. 3, June 2008.
[14] M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sörnmo,
"Clustering ECG Complexes Using Hermite Functions and Selforganizing
Maps," IEEE Transaction on Biomedical Engineering, Vol.
47, No. 7, pp.838-848, July, 2000.
[15] Berni, A. J., "Target identification by natural resonance estimation",
IEEE Transaction on Aerospace and Electronic Systems, Vol. AES-11,
No. 2, 147-154, 1975.
[16] H. S. Lui, N. V. Z. Shuley, "Radar target identification using a ÔÇÿbanded-
E-pulse technique," IEEE Transaction Antennas Propagation, Vol 54,
No. 12, pp. 3874-3881, 2006.
[17] Maes, Stephane Herman, "Monitoring, identification, and selection of
audio signal poles with characteristic behaviors, for separation and
synthesis of signal contributions" United States Patent 5930749, 1999
[18] F.M. El-Hefnawi, "Use of Prony-s method for extracting the poles and
zeros yielding a wideband window type response of circular antenna
arrays", Radio and Wireless Conference, RAWCON98, pp. 201-204,
Colorado/USA, Aug 1998.
[19] Huang J, Zhao J, Xie Y., "Source classification using pole method of AR
model", IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP '97), 1997
[20] Yingbo Hua, Tapan K. Sarkar, "A Discussion of E-Pulse Method and
Prony-s Method for Radar Target Resonance Retrieval from Scattered
Field", IEEE. Transaction Antennas Propagation, vol. 37, NO. 7, July
1989.
[21] Liao, S.P.; Fang, D.G.; Li, X.G. "Target feature extraction of frequency
domain data with optimal rational approximation", Antennas and
Propagation Society International Symposium, vol.1, Page(s):242 - 245,
18-25 Jul 1992.
[22] MIT-BIH Arrhythmia Database, www.physionet.org.
[23] P. deChazal, R. B. Reilly, G. McDarby, B. Celler, "Classification of the
electrocardiogram using selected wavelet coefficients and linear
discriminants", Proceeding 2000 IEEE International Conference
Acoustics, Speech and Signal Processing, pp. 3590-3593, Istanbul, 2000.
[24] T. Lobos, J. Rezmer, P. Schegner, "Parameter Estimation of Distorted
Signals Using Prony-s Method", IEEE Bologna Power Tech Conference,
Bologna/Italien, 2003.
[25] C. Ying, and R. Moses, "Prony-s Modeling of Linear FM Radar Data",
Spann Laboratory Technical Report TR-93-01, Ohio State University,
Jan. 1993.
[26] M. Alaoui Ismaili k A. Xémard, "Representation of electrical signals by
a series of exponential terms", IPST '99 International Conference on
Power Systems Transients, Budapest-Hungary, 1999.
[27] Hu, S., Wu, S. M. "Prony-s estimation of AR parameters of an ARMA
time series." Mechanical Systems and Signal Processing 3(2): 207-211.
1989.
[28] Siri Krishan Wasan, Vasudha Bhatnagar, Harleen Kaur, "The impact of
data mining techniques on medical diagnostics", Data Science Journal 5,
119-126, 2006.
[29] Abdel-Badeeh M. Salem, Kenneth Revett, and El-Sayed A. El-Dahshan,
"Machine Learning in Electrocardiogram Diagnosis", Proceedings of the
International Multi-conference on Computer Science and Information
Technology, pp. 429 - 433, 2009.
[30] E. A. El-Dahshan, A.B. M. Salem, T. H. Younis, "A Hybrid Technique
for Automatic MRI Brain Images Classification", Studia Univ. Babes-
Bolyai, Informatica, Vol. LIV, No. 1, 2009
[31] I. Jekova , G. Bortolan , I. Christov, "Assessment and comparison of
different methods for heartbeat classification", Medical Engineering &
Physics 30 , pp. 248-257, 2008.
[32] M. H. Song, J. Lee, H. D. Park, K. J. Lee, "Classification of Heartbeats
based on Linear Discriminant Analysis and Artificial Neural Network",
Proceedings of the 2005 IEEE Engineering in Medicine and Biology
27th Annual Conference Shanghai, China, September 1-4, 2005.
[33] Y. Wang, F. Agrafioti, D. Hatzinakos, and K. N. Plataniotis, "Analysis
of Human Electrocardiogram for Biometric Recognition", EURASIP
Journal on Advances in Signal Processing Volume 2008, Article ID
148658, 2008.
[34] V. Chud├í─ìek, G. Georgoulas, C. Stylios, M. Staviař, M. Hanuliak, and
L. Lhotská, "Comparison of Methods for Premature Ventricular Beat
Detection", ITAB 2006, Ioannina - Epirus, Greece, October 26-28, 2006.
[35] Recommended Practice for Testing and Reporting Performance Results
of Ventricular Arrhythmia Detection Algorithms (AAMI ECAR-1987).
Arlington, VA: Association for the Advancement of Medical
Instrumentation (AAMI). 1987.
[1] World Health organization," Cardiovascular diseases", Fact sheet
N┬░317, February 2007. http://www.who.int/cardiovascular_diseases.
[2] Rajendra Acharya U, Jasjit S. Suri, Jos A.E. Spaan, S .M. Krishnan,
"Advances in Cardiac Signal Processing", ISBN-13 978-3-540-36674-4
Springer Berlin Heidelberg New York, 2007.
[3] SW Chen. "Two-stage discrimination of cardiac arrhythmias using a
total least squares-based prony modeling algorithm" IEEE Transaction
on Biomedical Engineering, 47: pp. 1317-1326, 2000.
[4] Owis, M., Abou-Zied, A., Youssef, A.B., Kadah, Y., "Robust feature
extraction from ECG signals based on nonlinear dynamical modeling,"
23rd Annual International Conference IEEE Engineering in Medicine
and Biology Society. (EMBC-01). Volume 2. pp. 1585-1588, 2001.
[5] Dingfei Ge, Narayanan Srinivasan, Shankar Krishnan. "Cardiac
arrhythmia classification using autoregressive modeling" BioMedical
Engineering OnLine, 1(1):5, pp. 1585-1588, 2002.
[6] GE Ding-Fei, HOU Bei-Ping, and XIANG Xin-Jian, "Study of Feature
Extraction Based on Autoregressive Modeling in ECG Automatic
Diagnosis", ACTA Automation Sinica. Vol. 33 No. 5. pp. 462-466,
2007.
[7] P. de Chazal, M. O-Dwyer, and R. B. Reilly, "Automatic Classification
of Heartbeats Using ECG Morphology and Heartbeat Interval Features,"
IEEE Transaction on Biomedical Engineering, Vol. 51, No. 7, pp.1196-
1206, July 2004.
[8] Inan, O.T., Giovangrandi, L. and Kovacs, G.T.A., "Robust neuralnetwork-
based classification of premature ventricular contractions using
wavelet transform and timing interval features", IEEE Transaction on
Biomedical Engineering, Vol. 53, No.12. pp. 2507-2515, 2006.
[9] Ahmad R. Naghsh-Nilchi and A. Rahim Kadkhoda mohammadi,
"Cardiac Arrhythmias Classification Method Based on MUSIC,
Morphological Descriptors, and Neural Network", EURASIP Journal on
Advances in Signal Processing, Article No. 202. Volume 2008.
[10] Turker Ince, S. Kiranyaz, and M. Gabbouj, "A Generic and Robust
System for Automated Patient-specific Classification of
Electrocardiogram Signals", IEEE Transactions on Biomedical
Engineering, Vol. 56, No. 5, May 2009.
[11] S. S. Mehta, Nitin Shivappa Lingayat, "Support Vector Machine for
Cardiac Beat Detection in Single Lead Electrocardiogram". IAENG
International Journal of Applied Mathematics, pp. 1630-1635, 2007.
[12] B. Mohammad zadeh Asl, S.K. Setarehdan, "Neural Network Based
Arrhythmia Classification Using Heart Rate Variability Signal",
Proceedings of the 2nd International Symposium on Biomedical
Engineering, Bangkok, Thailand, Nov. 2006.
[13] B. Anuradha and V. C. Veera Reddy, "ANN for classification of cardiac
arrhythmias", ARPN Journal of Engineering and Applied Sciences, Vol.
3, No. 3, June 2008.
[14] M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sörnmo,
"Clustering ECG Complexes Using Hermite Functions and Selforganizing
Maps," IEEE Transaction on Biomedical Engineering, Vol.
47, No. 7, pp.838-848, July, 2000.
[15] Berni, A. J., "Target identification by natural resonance estimation",
IEEE Transaction on Aerospace and Electronic Systems, Vol. AES-11,
No. 2, 147-154, 1975.
[16] H. S. Lui, N. V. Z. Shuley, "Radar target identification using a ÔÇÿbanded-
E-pulse technique," IEEE Transaction Antennas Propagation, Vol 54,
No. 12, pp. 3874-3881, 2006.
[17] Maes, Stephane Herman, "Monitoring, identification, and selection of
audio signal poles with characteristic behaviors, for separation and
synthesis of signal contributions" United States Patent 5930749, 1999
[18] F.M. El-Hefnawi, "Use of Prony-s method for extracting the poles and
zeros yielding a wideband window type response of circular antenna
arrays", Radio and Wireless Conference, RAWCON98, pp. 201-204,
Colorado/USA, Aug 1998.
[19] Huang J, Zhao J, Xie Y., "Source classification using pole method of AR
model", IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP '97), 1997
[20] Yingbo Hua, Tapan K. Sarkar, "A Discussion of E-Pulse Method and
Prony-s Method for Radar Target Resonance Retrieval from Scattered
Field", IEEE. Transaction Antennas Propagation, vol. 37, NO. 7, July
1989.
[21] Liao, S.P.; Fang, D.G.; Li, X.G. "Target feature extraction of frequency
domain data with optimal rational approximation", Antennas and
Propagation Society International Symposium, vol.1, Page(s):242 - 245,
18-25 Jul 1992.
[22] MIT-BIH Arrhythmia Database, www.physionet.org.
[23] P. deChazal, R. B. Reilly, G. McDarby, B. Celler, "Classification of the
electrocardiogram using selected wavelet coefficients and linear
discriminants", Proceeding 2000 IEEE International Conference
Acoustics, Speech and Signal Processing, pp. 3590-3593, Istanbul, 2000.
[24] T. Lobos, J. Rezmer, P. Schegner, "Parameter Estimation of Distorted
Signals Using Prony-s Method", IEEE Bologna Power Tech Conference,
Bologna/Italien, 2003.
[25] C. Ying, and R. Moses, "Prony-s Modeling of Linear FM Radar Data",
Spann Laboratory Technical Report TR-93-01, Ohio State University,
Jan. 1993.
[26] M. Alaoui Ismaili k A. Xémard, "Representation of electrical signals by
a series of exponential terms", IPST '99 International Conference on
Power Systems Transients, Budapest-Hungary, 1999.
[27] Hu, S., Wu, S. M. "Prony-s estimation of AR parameters of an ARMA
time series." Mechanical Systems and Signal Processing 3(2): 207-211.
1989.
[28] Siri Krishan Wasan, Vasudha Bhatnagar, Harleen Kaur, "The impact of
data mining techniques on medical diagnostics", Data Science Journal 5,
119-126, 2006.
[29] Abdel-Badeeh M. Salem, Kenneth Revett, and El-Sayed A. El-Dahshan,
"Machine Learning in Electrocardiogram Diagnosis", Proceedings of the
International Multi-conference on Computer Science and Information
Technology, pp. 429 - 433, 2009.
[30] E. A. El-Dahshan, A.B. M. Salem, T. H. Younis, "A Hybrid Technique
for Automatic MRI Brain Images Classification", Studia Univ. Babes-
Bolyai, Informatica, Vol. LIV, No. 1, 2009
[31] I. Jekova , G. Bortolan , I. Christov, "Assessment and comparison of
different methods for heartbeat classification", Medical Engineering &
Physics 30 , pp. 248-257, 2008.
[32] M. H. Song, J. Lee, H. D. Park, K. J. Lee, "Classification of Heartbeats
based on Linear Discriminant Analysis and Artificial Neural Network",
Proceedings of the 2005 IEEE Engineering in Medicine and Biology
27th Annual Conference Shanghai, China, September 1-4, 2005.
[33] Y. Wang, F. Agrafioti, D. Hatzinakos, and K. N. Plataniotis, "Analysis
of Human Electrocardiogram for Biometric Recognition", EURASIP
Journal on Advances in Signal Processing Volume 2008, Article ID
148658, 2008.
[34] V. Chud├í─ìek, G. Georgoulas, C. Stylios, M. Staviař, M. Hanuliak, and
L. Lhotská, "Comparison of Methods for Premature Ventricular Beat
Detection", ITAB 2006, Ioannina - Epirus, Greece, October 26-28, 2006.
[35] Recommended Practice for Testing and Reporting Performance Results
of Ventricular Arrhythmia Detection Algorithms (AAMI ECAR-1987).
Arlington, VA: Association for the Advancement of Medical
Instrumentation (AAMI). 1987.
@article{"International Journal of Medical, Medicine and Health Sciences:54112", author = "Moustafa A. Bani-Hasan and Yasser M. Kadah and Fatma M. El-Hefnawi", title = "Identification of Cardiac Arrhythmias using Natural Resonance Complex Frequencies", abstract = "An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).", keywords = "Arrhythmias analysis, electrocardiogram, featureextraction, statistical classifiers.", volume = "4", number = "1", pages = "20-7", }
