Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network
- 1
- 2
- 3
Abstract
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.
- Barbara, J. (2006) Pitfalls and artifacts in electrocardiography. Cardiology Clinics, 24, 309-315. doi:10.1016/j.ccl.2006.04.006
- 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.
- 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.
- Issac, N.S., Shantha, S.K.R. and Sadasivam, V. (2005) Artificial neural network based automatic cardiac abnormalities classification. 6th International Conference, 41-46.
- 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.
- 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
- 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.
- http://www.organizedwisdom.com/Heart_disease
- Ronald, W.C. (1997) International handbook of arrhythmia. Informa Healthcare.
- World Health Organization (2005) The premise program: Prevention of recurrences of myocardial infarction and stroke study. WHO, 83, 801-880.
- Leo, S. (2006) An introduction to electrocardiography. Bleackwell Science.
- Wagner GS (2000) Marriot's practical electrocardiography. Williams & Wilkins.
- 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.
- http://www.physionet.org/physiobank/database/mitdb/index.htm
- 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