Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network
Himanshu Gothwal, Silky Kedawat, Rajesh Kumar
DOI: 10.4236/jbise.2011.44039   PDF    HTML     15,728 Downloads   34,405 Views   Citations


Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods.

Share and Cite:

Gothwal, H. , Kedawat, S. and Kumar, R. (2011) Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. Journal of Biomedical Science and Engineering, 4, 289-296. doi: 10.4236/jbise.2011.44039.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Barbara, J. (2006) Pitfalls and artifacts in electrocardiography. Cardiology Clinics, 24, 309-315. doi:10.1016/j.ccl.2006.04.006
[2] G.Karraz, G.M. (2006) Automatic classification of heartbeats using neural network classifier based on a bayesian framework. 28th Annual International Conference of the IEEE Publication, 4016-4019.
[3] Yu1, S.-N. and Chou, K.-T. (2006) Combining independent component analysis and backpropagation neural network for ECG beat classification. Proceeding of IEEE Engineering in Medicine and Biology Society, 1, 3090- 3093.
[4] Issac, N.S., Shantha, S.K.R. and Sadasivam, V. (2005) Artificial neural network based automatic cardiac abnormalities classification. 6th International Conference, 41-46.
[5] Alexakis, C., Nyongesal, H.O., Saatchi, R., Harris, N.D., Davies, C., Emery, C., Ireland, R.H. and Helle, S.R. (2003) Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia. Computers in Cardiology, 537-540.
[6] Poli, R., Cagnoni, S. and Valli, G. (1995) Genetic design of optimum linear and nonlinear QRS detectors. IEEE Transactiom Biomedical Engineering, 42, 1137-1141. doi:10.1109/10.469381
[7] Prasad, G.K. and Sahambi, J.S. (2003) Classification of ECG arrhythmias using multi-resolution analysis and neural networks. Proceedings of IEEE Conference on Convergent Technologies, 1, 227-231.
[9] Ronald, W.C. (1997) International handbook of arrhythmia. Informa Healthcare.
[10] World Health Organization (2005) The premise program: Prevention of recurrences of myocardial infarction and stroke study. WHO, 83, 801-880.
[11] Leo, S. (2006) An introduction to electrocardiography. Bleackwell Science.
[12] Wagner GS (2000) Marriot's practical electrocardiography. Williams & Wilkins.
[13] Dines, D.E. and Parkin, T.W. (1959) Some observations on P wave morphology in precordial lead V1 in patients with elevated left atrial pressures and left atrial enlargement. Proceedings of Staff Meeting Mayo Clinic, 34, 401.
[15] Uday, N.K., Rajni, K.R. and Melvin, M.S. (2006) The 12-lead electrocardiogram in supraventricular tachy- cardia. Cardiology Clinics, 24, 427-437. doi:10.1016/j.ccl.2006.04.004
[16] Hurst, J.W. (1998) Ventricular electrocardiography. J. B. Lippincott Company.
[18] Francis, M., June, E., William, J.B. and John, C. (2003) ABC of Electrocardiography. BMG publishing Group.
[19] John, M.M., Mithilesh, K.D., Anil, V.Y.D.B., Girish, N. and Cesar, A. (2006) Value of the 12-lead ECG in wide QRS tachycardia. Cardiology Clinics, 24, 439-451. doi:10.1016/j.ccl.2006.03.003
[20] Haykin, S. (2005) Neural networks—A comprehensive foundation. Prentice Hall.
[21] Fu, L.M. (2004) Neural networks in computer intelligence. McGraw-Hill Inc., 153-264.
[22] Hagan, M.T., and Menhaj, M.B. (1994) Training feed forward networks with the marquardt algorithm. IEEE Transactions on Neural Networks, 5, 989-993. doi:10.1109/72.329697
[23] Hu, Y.H., Palreddy, S. and Tompkins, W.J. (1997) A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44, 891-900. doi:10.1109/10.623058
[24] Minami, K., Nakajima, H. and Toyoshima, T. (1999) Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical Engineering, 46, 179-185. doi:10.1109/10.740880
[25] Osowski, S. and Lin, T.H. (2001) ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering, 48, 1265-1271. doi:10.1109/10.959322
[26] Owis, M.I., Youssef, A.B.M. and Kadah, Y.M. (2002) Characterization of ECG signals based on blind source separation. Medical & Biological Engineering & Computing, 40, 557-564. doi:10.1007/BF02345455

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.