Abstract
The ECG signal is a representation of bioelectrical activity of the heart’s pumping action. The doctor regularly uses a temporal recording of ECG and waveforms characteristics to study and diagnose the overall heart functioning. In some heart diseases, the correct diagnosis in an early time is essential for the patient survival. This need leads to the necessity to automate normal beat signals discrimination from abnormal beat signals. In our study, we have chosen the Multilayer Perceptron (MLP) as a classifier for this type of signals into two categories: normal (N) and pathological (V). To train this network, we used the database ”MIT BIH arrhythmia database.” This training is improved using a novel swarm optimization algorithm called Artificial Bees Colony (ABC) inspired from the foraging intelligence of honey bees. The (ABC) has the advantage of using fewer control parameters compared to other swarm optimization Algorithms. We propose several algorithms to filter, detect R peaks and extract the features of cardiac cycles to get it ready to be classified.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chalabi, Z., Berrached, N., Ilies, L.: Détection des Extrasystoles Ventriculaires par les Algorithmes SOM & LVQ. Colloque Télécom 2005 & 4èmes JFMMA, Rabat, Maroc, 23, 24 et 25 Mars (2005)
Pan, J., Tompkins, W.: A Real-Time QRS Detection Algorithm. IEEE Trans. on Biom. Eng. 32, 230–236 (1985)
Themis, P.E., Markos, G.T., Costas, P.E., Costas, P., Dimitrios, I.F., Lampros, K.M.: A methodology for the Automated Creation of Fuzzy Expert Systems for Ischemic and Arrhythmic Beat Classification based on a Set of Rules obtained by a Decision Tree. Journal Artif. Intelli. Medicine 40(3), 187–200 (2007)
Omer, T.I., Laurent, G., Gregory, T.A.K.: Robust Neural Network based Classification of Premature Ventricular Contractions using Wavelet Transform and Timing Interval Features. Journal IEEE Trans. on Biom. Engin. 53(12), 2507–2515 (2006)
Silipo, R., Marchesi, C.: Artifical Neural Networks for Automatic ECG Analysis. IEEE Trans. on Signal Processing 46(5) (May 1998)
Übeyli, E.D.: Analysis of ECG signals by diverse and composite features. Journal of Electrical & Electronics Eng. (2007)
Giovanni, B., Brohet, C., Fusaro, S.: Possibilities of using neural networks for ECG classification. Journal of Electrocardiology 29(suppl.) (1996)
Krasteva, V., Jekova, I.: Assessment of ECG Frequency and Morphology parameters for automatic classification of life-threatening cardiac arrhythmias. Journal of Physio. Measur. 26(5), 707–723 (2005)
Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Karaboga, D., Akay, B.: An artificial bee colony (abc) algorithm on training artificial neural networks. In: 15th IEEE Signal Processing and Communications Applications, SIU 2007, Eskisehir, Turkiye, pp. 1–4 (June 2007)
Karaboga, D., Akay, B., Ozturk, C.: Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007)
PhysioNet: Research Resource for Complex Physiolo- Signals, http://www.physionet.org
Tompkins, W.J.: Biomedical digital signal processing. University of Wisconsin-Madison (2000)
Keselbrener, L., Keselbrener, M., Akselrod, S.: Non-linear high pass for R-wave detection in ECG signal. Med. Eng. Phys. 19(5), 481–484 (1997)
Clifford, G.D., Azuaje, F., Patrick, E.: Advanced methods and tools for ECG Data Analysis. Artech house (2006)
Akay, B., Karaboga, D.: A modified Artificial Bee Colony algorithm for real-parameter optimization. Inform. Sci. (2010), doi:10.1016/j.ins.2010.07.015
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saadi, S., Bettayeb, M., Guessoum, A., Abdelhafidi, M.K. (2012). Artificial Bees Colony Optimized Neural Network Model for ECG Signals Classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-34478-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
Online ISBN: 978-3-642-34478-7
eBook Packages: Computer ScienceComputer Science (R0)