This paper presents an algorithm based on the
wavelet decomposition, for feature extraction from the ECG signal
and recognition of three types of Ventricular Arrhythmias using
neural networks. A set of Discrete Wavelet Transform (DWT)
coefficients, which contain the maximum information about the
arrhythmias, is selected from the wavelet decomposition. After that a
novel clustering algorithm based on nature inspired algorithm (Ant
Colony Optimization) is developed for classifying arrhythmia types.
The algorithm is applied on the ECG registrations from the MIT-BIH
arrhythmia and malignant ventricular arrhythmia databases. We
applied Daubechies 4 wavelet in our algorithm. The wavelet
decomposition enabled us to perform the task efficiently and
produced reliable results.
[1] Anant K., F. Dowla and G.Rodrigue, "Vector quantization of ECG
wavelet coefficients", IEEE Signal Processing Letters, Vol. 2, No. 7,
July, 1995.
[2] M.Vetterli, "Wavelets and filter banks: theory and design", IEEE
Transactions on Signal Processing, Sep, 1992, pp 2207 - 2232.
[3] R.M.Rao, A.S.Bopardikar, "Wavelet transforms: Introduction to theory
and applications", Addison Wesley Longman, 1998.
[4] L.Khadra, A.S.Al-Fahoum, H.Al-Nashash, "Detection of life threatening
cardiac arrhythmia using the wavelet transformation", Med. Biol. Eng.
Comput., Vol. 35, 1997, pp. 626-632.
[5] Addison P.S., Watson J.N., Clegg G.R., Holzer M., Sterz F. and
Robertson C.E., ÔÇÿEvaluating arrhythmias in ECG signals using wavelet
transforms-, IEEE Engineering in Medicine and Biology Magazine, Vol.
19, pp. 104-109, 2000.
[6] Dinh H.A.N., Kumar D.K., Pah N.D. and Burton P., ÔÇÿWavelets for QRS
detection-, Proceedings of the 23rd Annual Conference, IEEE EMS,
Istanbul, Turkey, pp. 35-38, 2001.
[7] Kadambe S., Murray R. and Boudreaux-Bartels G.F., ÔÇÿWavelet
transform based QRS complex detector-, IEEE Transaction on
Biomedical Engineering, Vol. 46, No. 7, pp. 838-848, 1999.
[8] Romero I., Serrano L. and Ayesta, ÔÇÿECG frequency domain features
extraction: A new characteristic for arrhythmias classification-,
Conference of the IEEE Engineering in Medicine and Biology Society,
2001.
[9] Szilagyi S.M. and Szilagyi L., ÔÇÿWavelet Transform and Neural Network
based Adaptive Filtering for QRS Detection-, Proceedings of World
Congress on Medical Physics and Biomedical Engineering, Chicago,
USA, 2000.
[10] Rumelhart D.E., Hinton G.E. and Williams R.J., "Learning
representations by back-propagation errors", Nature, 1986.
[11] V.X.Afonso, W.J.Tompkins, "Detecting ventricular fibrillation", IEEE
Eng. Boil., March/April, 1995, pp. 152-159.
[12] Selvakumar G., Bhoopathy Bagan K. and Chidhambara Rajan B.,
ÔÇÿWavelet Decomposition for Detection and Classification of Critical
ECG Arrhythmias-, Proc. of the 8th WSEAS Int. Conf. on Mathematics
And Computers in Biology and Chemistry, Vancouver, Canada, June 19-
21, 2007.
[13] A.S. Al-Fahoum, I.Howitt, "Combined wavelet transformation and
radial basis neural networks for classifying life threatening cardiac
arrhythmias", Med. Biol. Eng. Comput., Vol. 37, 1999, pp. 566 - 573.
[14] MIT-BIH (http://www.physionet.org)
[15] Dorigo M., Caro GD, Gambardella L.M. Ant algorithms for discrete
optimization. Artiff Life 1999;5, pp 137-142
[16] Dorigo M, Maniezzo and Colomi A. " Ant system: optimization by a
clolony of cooperating agents", IEEE Trans. Systems, Man and
Cybernetics- part B, Vol 26, pp 29-41 Feb 1996
[17] Tsai C-F, Tsai C-W, Wu H-C Yang T . A novel data clustering approach
for data mining in large databases, Journal of System and Software, 2004
73:133-45
[1] Anant K., F. Dowla and G.Rodrigue, "Vector quantization of ECG
wavelet coefficients", IEEE Signal Processing Letters, Vol. 2, No. 7,
July, 1995.
[2] M.Vetterli, "Wavelets and filter banks: theory and design", IEEE
Transactions on Signal Processing, Sep, 1992, pp 2207 - 2232.
[3] R.M.Rao, A.S.Bopardikar, "Wavelet transforms: Introduction to theory
and applications", Addison Wesley Longman, 1998.
[4] L.Khadra, A.S.Al-Fahoum, H.Al-Nashash, "Detection of life threatening
cardiac arrhythmia using the wavelet transformation", Med. Biol. Eng.
Comput., Vol. 35, 1997, pp. 626-632.
[5] Addison P.S., Watson J.N., Clegg G.R., Holzer M., Sterz F. and
Robertson C.E., ÔÇÿEvaluating arrhythmias in ECG signals using wavelet
transforms-, IEEE Engineering in Medicine and Biology Magazine, Vol.
19, pp. 104-109, 2000.
[6] Dinh H.A.N., Kumar D.K., Pah N.D. and Burton P., ÔÇÿWavelets for QRS
detection-, Proceedings of the 23rd Annual Conference, IEEE EMS,
Istanbul, Turkey, pp. 35-38, 2001.
[7] Kadambe S., Murray R. and Boudreaux-Bartels G.F., ÔÇÿWavelet
transform based QRS complex detector-, IEEE Transaction on
Biomedical Engineering, Vol. 46, No. 7, pp. 838-848, 1999.
[8] Romero I., Serrano L. and Ayesta, ÔÇÿECG frequency domain features
extraction: A new characteristic for arrhythmias classification-,
Conference of the IEEE Engineering in Medicine and Biology Society,
2001.
[9] Szilagyi S.M. and Szilagyi L., ÔÇÿWavelet Transform and Neural Network
based Adaptive Filtering for QRS Detection-, Proceedings of World
Congress on Medical Physics and Biomedical Engineering, Chicago,
USA, 2000.
[10] Rumelhart D.E., Hinton G.E. and Williams R.J., "Learning
representations by back-propagation errors", Nature, 1986.
[11] V.X.Afonso, W.J.Tompkins, "Detecting ventricular fibrillation", IEEE
Eng. Boil., March/April, 1995, pp. 152-159.
[12] Selvakumar G., Bhoopathy Bagan K. and Chidhambara Rajan B.,
ÔÇÿWavelet Decomposition for Detection and Classification of Critical
ECG Arrhythmias-, Proc. of the 8th WSEAS Int. Conf. on Mathematics
And Computers in Biology and Chemistry, Vancouver, Canada, June 19-
21, 2007.
[13] A.S. Al-Fahoum, I.Howitt, "Combined wavelet transformation and
radial basis neural networks for classifying life threatening cardiac
arrhythmias", Med. Biol. Eng. Comput., Vol. 37, 1999, pp. 566 - 573.
[14] MIT-BIH (http://www.physionet.org)
[15] Dorigo M., Caro GD, Gambardella L.M. Ant algorithms for discrete
optimization. Artiff Life 1999;5, pp 137-142
[16] Dorigo M, Maniezzo and Colomi A. " Ant system: optimization by a
clolony of cooperating agents", IEEE Trans. Systems, Man and
Cybernetics- part B, Vol 26, pp 29-41 Feb 1996
[17] Tsai C-F, Tsai C-W, Wu H-C Yang T . A novel data clustering approach
for data mining in large databases, Journal of System and Software, 2004
73:133-45
@article{"International Journal of Medical, Medicine and Health Sciences:64606", author = "A.Sankara Subramanian and G.Gurusamy and G.Selvakumar and P.Gnanasekar and A.Nagappan", title = "ECG Analysis using Nature Inspired Algorithm", abstract = "This paper presents an algorithm based on the
wavelet decomposition, for feature extraction from the ECG signal
and recognition of three types of Ventricular Arrhythmias using
neural networks. A set of Discrete Wavelet Transform (DWT)
coefficients, which contain the maximum information about the
arrhythmias, is selected from the wavelet decomposition. After that a
novel clustering algorithm based on nature inspired algorithm (Ant
Colony Optimization) is developed for classifying arrhythmia types.
The algorithm is applied on the ECG registrations from the MIT-BIH
arrhythmia and malignant ventricular arrhythmia databases. We
applied Daubechies 4 wavelet in our algorithm. The wavelet
decomposition enabled us to perform the task efficiently and
produced reliable results.", keywords = "Daubechies 4 Wavelet, ECG, Nature inspired
algorithm, Ventricular Arrhythmias, Wavelet Decomposition.", volume = "5", number = "12", pages = "703-5", }
