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Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification


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A. H. Sahoolizadeh, B. Z. Heidari, and C. H. Dehghani, “A New Face Recognition Method using PCA, LDA and Neural Network,”International Journal of Computer Science and Engineering, vol. 2, no. 4, pp. 218–223, 2008. Search in Google Scholar

D. Zhang, G. L. G. Lu, W. L. W. Li, L. Z. L. Zhang, and N. L. N. Luo, “Palmprint Recognition Using 3-D Information,”IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, vol. 39, no. 5, pp. 505-519, 2009.10.1109/TSMCC.2009.2020790 Search in Google Scholar

F. Agrafioti, F. M. Bui, and D. Hatzinakos, “Medical biometrics in mobile health monitoring,”Security and Communication Networks, vol. 4, no. 5, pp. 525-539, 2011.10.1002/sec.227 Search in Google Scholar

Y. Wang, F. Agrafioti, D. Hatzinakos, and K. N. Plataniotis, “Analysis of Human Electrocardiogram for Biometric Recognition,”EURASIP Journal on Advances in Signal Processing, vol. 2008, no. 1, pp. 1-12, 2008. Search in Google Scholar

Y. Wang, K. N. Plataniotis, D. Hatzinakos, T. Edward, S. R. Sr, and C. Engineering, “Integrating Analytic And Appearance Attributes For Human Identification From Ecg Signals,” in IEEE Biometrics Symposium, 2006.10.1109/BCC.2006.4341627 Search in Google Scholar

N. Belgacem, “ECG Based Human Authentication using Wavelets and Random Forests,”International Journal on Cryptography and Information Security, vol. 2, no. 2, pp. 1-11, Jun. 2012.10.5121/ijcis.2012.2201 Search in Google Scholar

R. D. Labati, A. Genovese, V. Piuri, and F. Scotti, “Wildfire Smoke Detection using Computational Intelligence Techniques Enhanced with Synthetic Smoke Plume Generation,”Systems, Man, and Cybernetics: Systems, IEEE Transactions on, vol. PP , Issue, pp. 1 - 10, 2012.10.1109/CIMSA.2011.6059930 Search in Google Scholar

A. Bouchachia and N. Nedjah, “Adaptive Incremental Learning In Neural Networks,”Neurocomputing, vol. 74, no. 11, pp. 1783-1784, May 2011. Search in Google Scholar

H. Atoui, J. Fayn, and P. Rubel, “A novel neural-network model for deriving standard 12- lead ECGs from serial three-lead ECGs: application to self-care.,”IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society, vol. 14, no. 3, pp. 883-90, May 2010.10.1109/TITB.2010.2047754 Search in Google Scholar

B. M. Wilamowski and H. Yu, “Neural network learning without backpropagation.,”IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 21, no. 11, pp. 1793-803, Nov. 2010. Search in Google Scholar

X. Liu and J. Cao, “Robust State Estimation For Neural Networks With Discontinuous Activations,”IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society, vol. 40, no. 6, pp. 142537, Dec. 2010. Search in Google Scholar

T. Kohonen, “Learning-Vector Quantization and the Self-Organizing Map,” in Theory and Applications of Neural Networks, 1992, pp. 235-242.10.1007/978-1-4471-1833-6_15 Search in Google Scholar

A. Sato and K. Yamada, “Generalized Learning Vector Quantization,” in Advances in Neural Information Processing Systems 8 Proceedings of the 1995 Conference, 1996, vol. 7, pp. 423-429. Search in Google Scholar

F.-michaelSchleif, T. Villmann, B. Hammer, P. Schneider, and M. Biehl, “Generalized derivative based kernelized Learning Vector Quantization,”Springer, Intelligent Data Engineering and Automated Learning – IDEAL 2010 Lecture Notes in Computer Science, vol. 6283, pp. pp 21-28, 2010. Search in Google Scholar

K. Marika, B. Hammer, M. Biehl, and T. Villmann, “Functional relevance learning in generalized learning vector quantization,”Neurocomputing vol. 90, pp. 85-95, 2012.10.1016/j.neucom.2011.11.029 Search in Google Scholar

B. Hammer, M. Strickert, and T. Villmann, “On The Generalization Ability of GRLVQ Networks,”Neural Processing Letters, pp. 109-120, 2005.10.1007/s11063-004-1547-1 Search in Google Scholar

B. Kusumoputro, H. Budiarto, and W. Jatmiko, “Fuzzy-neuro LVQ and Its Comparison with Fuzzy Algorithm LVQ In Artificial Odor Discrimination System,”ISA Transactions, pp. 395-407, 2002.10.1016/S0019-0578(07)60097-4 Search in Google Scholar

I. M. A. Setiawan, E. M. Imah, and W. Jatmiko, “Arrhytmia Classification using Fuzzy- Neuro Generalized Learning Vector Quantization,” in IEEE International Conference on Advanced Computer Science and Information System 2011 (ICACSIS 2011), 2011, pp. 978-979. Search in Google Scholar

E. Mwebaze et al., “Neurocomputing Divergence-based classification in learning vector quantization,”Neurocomputing, vol. 74, no. 9, pp. 1429-1435, 2011. Search in Google Scholar

E. M. Imah, W. Jatmiko, and T. Basaruddin, “Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) As New Algorithm With Integrating Feature Extraction And Classification For Arrhythmia Heartbeats Classification,” in IEEE international Conference on System Man and Cybernetics, Seoul 2012, 2012.10.1109/ICSMC.2012.6377692 Search in Google Scholar

S. García and F. Herrera, “Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy.,”Evolutionary computation, vol. 17, no. 3, pp. 275-306, Jan. 2009.10.1162/evco.2009.17.3.275 Search in Google Scholar

B. X. Wang and N. Japkowicz, “Boosting support vector machines for imbalanced data sets,”Knowledge and Information Systems, vol. 25, no. 1, pp. 1-20, 2009.10.1007/s10115-009-0198-y Search in Google Scholar

N. Japkowicz, “Learning from Imbalanced Data Sets: A Comparison of Various Strategies,” in Proc. Am. Assoc. for Artificial Intelligence (AAAI) Workshop, 2000, vol. 68. Search in Google Scholar

C. Vivaracho-pascual and A. Simon-hurtado, “Improving ANN performance for imbalanced data sets by means of the NTIL technique,”IEEE International Join Conference on Neural Networks (IJCNN), 2010.10.1109/IJCNN.2010.5596885 Search in Google Scholar

G. de Lannoy, D. Francois, J.Delbeke, and M. Verleysen, “Weighted conditional random fields for supervised interpatient heartbeat classification,”IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 241-7, Jan. 2012.10.1109/TBME.2011.2171037 Search in Google Scholar

X. Wang and K. K. Paliwal, “Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition,”Pattern Recognition, vol. 36, no. 10, pp. 2429-2439, Oct. 2003. Search in Google Scholar

A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet,”Circulation, vol. 101, no. 23, p. 215, 2000.10.1161/01.CIR.101.23.e215 Search in Google Scholar

J. A. Van Alsté, W. Van Eck, and O. E. Herrmann, “ECG baseline wander reduction using linear phase filters.,”Computers and biomedical research an international journal, vol. 19, no. 5, pp. 417-427, 1986.10.1016/0010-4809(86)90037-6 Search in Google Scholar

C. R. Meyer, “Electrocardiogram Using Cubic Baseline Noise Estimation and Removal Splines and State-Space Computation Techniques”Computers and Biomedical Research, 1977.10.1016/0010-4809(77)90021-0 Search in Google Scholar

F. Badilini, A. J. Moss, and E. L. Titlebaum, “Cubic Spline Baseline Estimation In Ambulatory Ecg Recordings,”Engineering In Medicine And Biology, vol. 13, no. 2, pp. 584-585, 1991. Search in Google Scholar

A. Rakotomamonjy, R. Flamary, and F. Yger, “Learning with infinitely many features,”Machine Learning, vol. 91, no. 1, pp. 43-66, DApril 2013.10.1007/s10994-012-5324-5 Search in Google Scholar

J. Weng, Cheng G, Poon, “A New Evaluation Measure for Imbalanced Datasets,” in Seventh Austraasian Data Mining Conference (AusDM 2008), 2008.Search in Google Scholar

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