Abstract
In this paper we describe a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal heartbeats. These were segmented and transformation and 3 methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani type fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.
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References
Acharya, Kumar, Bhat: Classification of cardiac abnormalities using heart rate signals. Medical & Biological Enginnering & Computing (2004)
Alzate, A., Giraldo, E.: Clasificación de Arritmias utilizando ANFIS, Redes Neuronales y Agrupamiento Substractivo (2006)
Kulkarni, A.: Computer Vision and Fuzzy-Neural Systems, PH PTR
Azam, F.: Biologically Inspired Modular Neural Networks, Electrical and Computer Engineering. Blacksburg, Virginia (2004)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1996)
del Brío, B.M., Molina, A.S., Neuronales, R., Borrosos, S.: 3a Edición, Alfaomega Ra-Ma (2007)
Anuradha, B., Suresh Kumar, K., Veera Reddy, V.C.: Classification of Cardiac Signals using Time Domain Methods (2008)
Anuradha, B., Veera Reddy, V.C.: ANN for Classification of Cardiac Arrhythimias (2008)
Chuang, C.: Using Discrete Wavelet Transform of ECG Signals for Personal Identity Verification (2005)
Patra, D., Das, M.K., Pradhan, S.: Integration of Fcm, Pca and Neural Networks for Classification of Ecg Arrhythmias (2009)
Fahlman, S.: Faster Learning Variations of Backpropagation: An Empirical Study. In: Touretzky, D.S., Hinton, G.E., Sejnowski, T.J. (eds.) Proceedings of the 1988 Connectionist Models Summer School. Morgan Kaufmann Publishers, Los Altos (1988)
Freeman, J.A.: Simulating Neural Networks with Mathematica. Addison-Wesley, Reading (1994)
Clifford, G., Azuaje, F., McSharry, P.: Advanced Methods and Tools for ECG Data Analysis. Engineering Medicine & Biology. Artech House, Boston-London (2006)
Heart Health, National Geographic, http://yourtotalhealth.ivillage.com/
Nabney, I.T., Evans, D.J., Tenner, J., Gamlyn, L.: Benchmarking Beat Classification Algorithms
Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. (1985)
Keller, J.M., Gray, M.R., Givens Jr., J.A.: A Fuzzy K-Nearest Neighbor Algorithm (1985)
Jang, J.-S.R., Sum, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs (1997)
Khadra, Al-Fahoum, Al-Nashash: Detection of life-threatening cardiac arrhythmias using wavelets transformation. Med. Biol. Eng. Comput. (1997)
Barbosa, L., Kleisinger, G.H., Valdez, A.D., Monzón, J.E.: Utilización del Modelo Kohonen y del Perceptrón Multicapa para detectar Arritmias Cardiacas (2001)
Tsipouras, M., Fotiadis, D.: An Efficient System for the Detection of Arrhythmic Segments in ECG Recordings based on non-Linear Features of the RR Interval Signal (September 2003)
Engin, M.: ECG Beat Classification using neuro-fuzzy network. Elsevier, Amsterdam (2004)
Cepek, M., Chudácek, V., Petrik, M., Geogoulas, G., Stylios, C., Lhotská, L.: Comparison of Inductive Modeling Method to other Classification Methods for Holter ECG
MIT-BIH Arrhythmia Database. PhysioBank, Physiologic Signal Archives for Biomedical Research, http://www.physionet.org/physiobank/database/mitdb/
O’ Dwyer, M., de Chazal, P., Reilly, R.B.: Beat Classification for Use in Arrhythmia Analysis (2000)
Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M.: ECG pattern recognition and classification using non-linear transformations and neural networks (1998)
Belgacem, N., Chikh, M.A., Bereksi Reguig, F.: Supervised Classification of ECG using Neural Networks
Neural Network for Mathematica, Wolfram Research Inc., Chicago, IL (2003)
de Chazal, P., Reilly, R.B.: Automatic Classification of ECG Beats using Wareform Shape and Heart Beat Interval Features (1998)
Ceylan, R., Ozbay, Y., Karlik, B.: A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network (2009)
Rogal, S., Paraiso, E., Kaestner, C., Figueredo, M., Neto, A.: Agrupamiento de Arritmias Cardiacas Utilizando ART2 (2008)
Reghav, S., Amit, K.: Fractal Feature Based ECG Arrhythmia Classification (November 2008)
Mehta, S.S., Lingayat, N.S.: Identification of QRS Complexes in 12-lead Electrocardiogram. Elsevier, Amsterdam (2009)
Ari, S., Saha, G.: In Search of an optimization technique for Artificial Neuronal Network to Classify abnormal heart sounds (2009)
Samarasinghe, S.: Neural Networks for Applied Science and Engineering. Auerback Publications (2007)
Sun, Y., Chan, K.: Arrhythmia detection and recognition in ECG Signals using nonlinear techniques, Ann. Biomed. Eng. (2000)
Yu, S.-N., Chen, Y.-H.: Selection of Higher Order Subband Features for ECG Beat Classification (2008)
Palreddy, S.: ECG Beats Database Description, PH. D. University of Wisconsin (1996)
Werbos, P.J.: The Roots of Backpropagation Forom Ordered Derivatives to Neuronal Networks and Political Forescasting. Wiley Series on Adaptive and Learning System for Signal Processing Communication and Control. Wiley, New York (1994)
Ozbay, Y., Ceylan, R., Karlik, B.: A Fuzzy Clustering Neuronal Network Architecture for Classification of ECG Arrhtymias (January 2005)
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Ramírez, E., Castillo, O., Soria, J. (2010). Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15111-8_3
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DOI: https://doi.org/10.1007/978-3-642-15111-8_3
Publisher Name: Springer, Berlin, Heidelberg
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