MITAutomatic ECG Beat Tachycardia Detection Using Artificial Neural Network
The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.
[1] PHW. Leong, "A Low Power VLSI Arrhythmia Clasifier" in IEEE
Transaction on neural networks, vol. 6,No 6, 1995, pp. 1435-1445.
[2] Gari D. Clifford, Signal Processing Methods For Heart Rate Variability
Analysis, University of Oxford, 2002.
[3] A.L. Goldberger, L.A.N Amaral, L. Glass, J.M. Hausdorff, P.Ch.
Ivanov, R.G. Mark, J.E. Mietus G.B. Moody, C.K.Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet, Components of a New Research Resource for Complex Physiologic Signals, American Heart
Association, 2000.
[4] http://www.physionet.org/physiobank.
[5] J Pan, WJ Tompkins, A real time QRS detection algorithm, Biomed Eng
IEEE Trans, vol. 3,1985,pp. 230-236.
[6] JP Sahoo, Analysis of ECG signal for detection of cardiac arrhythmias,
2011.
[7] A Graps, An Introduction to wavelets, Computation Science &
Engineering IEEE, vol.2, 1995, pp. 50-61.
[8] PS Addison, Wavelet transform and the ECG, Physiol, Meas 2005,pp.
155-199.
[9] I Daubechies, Ten Lectures on wavelets, SIAM, 1992.
[10] M. Elgendi, R wave detection using coiflets wavelets, Bioengineering
Conference, IEEE 35th Annual Northeast, 2009.
[11] V. Kecman, Learning and Soft Computing Support vector machines-
Neural Networks-Fuzzy Logic, MIT, 2001.
[12] DF Spetch, Probabilistic Neural Network, Neural Networks, vol. 3,
1990, pp. 109-118.
[13] HA Guvenir, B Acar, Feature Selection using a Genetic Algorithm for
the detection of abnormal ECG recordings, International Conference on
Machine Learning,Florida, 2001.
[14] W Zhu,N Zeng, N Wang, Sensitivity-Specificity-Accuracy Associated
and ROC Analysis with practical SAS implementation,NEGUS,2010.
[15] TM Nazmy, H El.Messiry, F Al-Bokhity, Adaptive Neuro-Fuzzy
Inference system for classification of ECG signals, Journal of
Theoretical and Applied Information Technology, 2009.
[16] F Belhachat, N Izoboudjen, Application of a probabilistic neural
network for classification of cardiac arrhythmias, 13th International Research/ Expert Conference" Trends in the development of machineryand associated technology", Tunisia, 2009.
[17] D Gao, M Madden, Arrhythmia Identification from ECG Signals with a
Neural Network Classifier Based on a Bayesian Framework, 24th SGAI
International Conference on Innovative Techniques and Applications of
Artificial Intelligence, 2004.
[1] PHW. Leong, "A Low Power VLSI Arrhythmia Clasifier" in IEEE
Transaction on neural networks, vol. 6,No 6, 1995, pp. 1435-1445.
[2] Gari D. Clifford, Signal Processing Methods For Heart Rate Variability
Analysis, University of Oxford, 2002.
[3] A.L. Goldberger, L.A.N Amaral, L. Glass, J.M. Hausdorff, P.Ch.
Ivanov, R.G. Mark, J.E. Mietus G.B. Moody, C.K.Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet, Components of a New Research Resource for Complex Physiologic Signals, American Heart
Association, 2000.
[4] http://www.physionet.org/physiobank.
[5] J Pan, WJ Tompkins, A real time QRS detection algorithm, Biomed Eng
IEEE Trans, vol. 3,1985,pp. 230-236.
[6] JP Sahoo, Analysis of ECG signal for detection of cardiac arrhythmias,
2011.
[7] A Graps, An Introduction to wavelets, Computation Science &
Engineering IEEE, vol.2, 1995, pp. 50-61.
[8] PS Addison, Wavelet transform and the ECG, Physiol, Meas 2005,pp.
155-199.
[9] I Daubechies, Ten Lectures on wavelets, SIAM, 1992.
[10] M. Elgendi, R wave detection using coiflets wavelets, Bioengineering
Conference, IEEE 35th Annual Northeast, 2009.
[11] V. Kecman, Learning and Soft Computing Support vector machines-
Neural Networks-Fuzzy Logic, MIT, 2001.
[12] DF Spetch, Probabilistic Neural Network, Neural Networks, vol. 3,
1990, pp. 109-118.
[13] HA Guvenir, B Acar, Feature Selection using a Genetic Algorithm for
the detection of abnormal ECG recordings, International Conference on
Machine Learning,Florida, 2001.
[14] W Zhu,N Zeng, N Wang, Sensitivity-Specificity-Accuracy Associated
and ROC Analysis with practical SAS implementation,NEGUS,2010.
[15] TM Nazmy, H El.Messiry, F Al-Bokhity, Adaptive Neuro-Fuzzy
Inference system for classification of ECG signals, Journal of
Theoretical and Applied Information Technology, 2009.
[16] F Belhachat, N Izoboudjen, Application of a probabilistic neural
network for classification of cardiac arrhythmias, 13th International Research/ Expert Conference" Trends in the development of machineryand associated technology", Tunisia, 2009.
[17] D Gao, M Madden, Arrhythmia Identification from ECG Signals with a
Neural Network Classifier Based on a Bayesian Framework, 24th SGAI
International Conference on Innovative Techniques and Applications of
Artificial Intelligence, 2004.
@article{"International Journal of Medical, Medicine and Health Sciences:51373", author = "R. Amandi and A. Shahbazi and A. Mohebi and M. Bazargan and Y. Jaberi and P. Emadi and A. Valizade", title = "MITAutomatic ECG Beat Tachycardia Detection Using Artificial Neural Network", abstract = "The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.", keywords = "Fuzzy Logic, Probabilistic Neural Network, Tachycardia, Wavelet Transform.", volume = "6", number = "11", pages = "539-4", }