In this study, we have presented a method for detecting four common arrhythmias by using wavelet analysis along with the neural network algorithms. The method firstly includes the extraction of feature vectors with wavelet analysis. Then, the vectors will be categorized by means of the neural network into four classes. Input signals are recorded from two different leads. In addition, we have used both continuous and discrete wavelet analyses simultaneously for feature extraction. This results into increasing the accuracy of feature vectors extraction. Also, using the continuous wavelet in a specific scale can lead to better extraction of coefficients as well as more accurate data. In order to decrease the computational efforts and increase the training speed, the dimensions of the feature vectors have been reduced by substituting the wavelet coefficients with their statistical parameters. Furthermore, two approaches are introduced in classification of feature vectors. The first approach comprises four neural networks in the parallel form for detection of four classes, while the second approach makes use of one network for four classes. Numerical simulation results show that in comparison with the previous studies, the proposed methods are more accurate and faster. In addition, it is observed that the second approach has better capabilities in classification of data than the first one. On the other hand, the first approach is believed to have a good function for complicated data spaces.
How to Cite
feature extraction, Wavelet Analysis, ECG signal, Heart arrhythmia, Neural network
Al-Fahoum, A.S. & Howitt, I. (1999), Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Medical and Biological Engineering and Computing, 37: 566–573.
Al-Nashash, H. (2000), Cardiac arrhythmia classification using neural networks. Techno Health Care, 8:363–72.
Casaleggio, A., Braiotta, S. (1997). Estimation of Lyapunov exponents of ECG time series-the influence of parameters. Chaos, Solitons & Fractals, 8 (10):1591- 1599.
Ceylan, R., Ozbay, Y. (2007), Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Systems with Applications, 33: 286–295.
Christov, I. , Jekova, I., Bortolan, G. (2005). Premature ventricular contraction classification by the Kth nearest neighbors rule. Physiol Meas, 26:123–30.
Engin, M. & Demirag, S. (2003). Fuzzy-hybrid neural network based ECG beat recognition using three different types of feature set. Cardiovascular Engineering: An International Journal, 3(2): 1–80.
Engin, M. (2004). ECG beat classification using neuro- fuzzy network. Pattern Recognition Letters, 25: 1715– 1722.
Foo, S. Y., Stuart, G., Harvey, B. & Meyer-Baese, A. (2002). Neural network-based ECG pattern recognition. Engineering Applications of Artificial Intelligence, 15: 253–260.
Ghaffari , A., Golbayani, H. (2008), A new mathematical based QRS detector using continuous wavelet transform, Computer & Electrical Engineering, 34(2): 81-91.
Guler, I., Ubeyli, E. (2005), A modified mixture of exports network structure for ECG beats classification with diverse features. Engineering Application of Artificial Intelligence, 18: 845-856.
Hu, Y.H. , Palreddy, S. & Tompkins, W.J. (1997). A Patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering,44(9): 891–900.
Jekova, I., Bortolan, G., Chridstov, I. (2007). Assessment and comparison of different methods For heartbeat classification. Med Eng Phys (2007).
Minami, K., Nakajima, H. & Toyoshima, T. (1999). Real- time discrimination of ventricular tachyarrhythmia with Fourier transform neural network. IEEE Transactions on Biomedical Engineering, 46(2): 179–185.
Shahidi Zandi, A., Moradi, M. H. (2006), Quantitative evaluation of a wavelet-based method in ventricular late potential detection. Pattern Recognition, 39: 1369- 1379.
Sung-Nien, Y ., Kuan-T o, Ch. (2007), Integration of independent component analysis and neural networks for ECG beat classification. Expert Systems with Applications, 34, 2841–2846.
Ubeyli, E.D., Gular, I. (2004). Detection of electrocardiogarphic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks. Engineering Applications of Artificial Intelligence, 17(6): 567-576.
Wang, Z. , He, Z. & Chen, J. Z. (1997). Blind EGG separation using ICA neural networks. In Proceedings- 19th annual international conference of the IEEE- EMBS, Vol. 3 (pp. 1351–1354). Chicago, IL, USA.
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