[1] S. Srivasta, and H. Bhardwaj, “ECG Pattern Analysis using Artificial Neural Network,” SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) , Vol. 7, pp. 1-4, May 2020.
[2] G. Yao, and X. Mao, “Interpretation of Electrocardiogram Heartbeat by CNN and GRU,” Computational and Mathematical Methods in Medicine, Vol. 2, pp. 1-10, 2021.
[3] O. Berenfeld, and J. F. Rodriguez Matas, “Editorial: Atrial Filrillation: Technology for Diagnosis, Monitoring, and traetment,” Frontiers in Physialogy , Vol. 13, pp. 1-4, Feb 2022.
[4] W. Schleifer, and K. Sirvathsan, “Ventricular Arrhythmias State of Art,” Cardiol Clin, Vol. 31, pp. 595-605, 2012.
[5] N. V. Goonewardane, and Ang S. Kottegoda, “SupperVentricular Tachycardia,” Journal of Arrhythmia, Vol. 35, pp. 641-692, 2019.
[6] B. Anuragha, and K. Suresh Kumar, “Classification of cardiac signals using time domain methods,” ARPN Journal of Engineering and Applied Science, Vol. 3, pp. 7-12, 2008.
[7] J. Cai, and G. Zhou, “Real-Time Arrhythmia Classification Algorithm using Time-Domain ECG Feature Based on FFNN and CNN,” Mathematival Problems in Engineering, Vol. 2021, pp. 1-17, 2021.
[8] H. Gothwal, and S. Kedawat, “Cardiac arrhythmia detection in an ECG beat signal using fast Fourier transform and artificial neural network,” J. Biomedical Science and Engineering, Vol. 4, pp. 289-296, 2011.
[9] C. H. Lin, “Frequency–domain features for ECG discrimination using gray relational analysis-based classifier,” Computer & Mathematics with Applications, Vol. 55, pp. 680-690, 2008.
[10] S. Z. Mahmoodabadi, and A. Ahmadian, “ECG feature extraction using Daubechies wavelets,” Proceeding of the fifth IASTED international conference on visualization, imaging and image processing., Puerto Vallarta, Mexico., IVIP. 2005, pp. 343-348.
[11] Q. Zhao, and L. Zhan, “ECG feature extraction and classification using wavelet transforms and support vector machines,” International conference on neural networks and brain., ICNN&B. 2005, pp. 1086-1092.
[12] B. A. Sabrine, and A. Taoufik, “Arrhythmia Classification Using Fractal Dimensions and Neyral Networks,” Advances in Intelligent Systems Research, Vol. 175,pp. 182-187, 2021.
[13] K. Kiani, and F. Maghsoudi, “Classification of 7 Arrhythmias from ECG using Fractal Dimensions,” J Bioinform Syst Biol, Vol. 2, pp. 053-065, 2019.
[14] A. Casaleggio, and A. Corana, “Study of the Lyapunov exponents of ECG signals from MIT-BIH database,” IEEE Conference: Computers in Cardiology., Zaragoza., CinC. 1995, pp. 697-800.
[15] O. Gorshkov, and H. Ombao, “Multi-chaotic Analysis of Inter-Beat (R-R) Intervals in Cardiac Signals for Discrimination between Normal and Pathological Classes,” Entripy, Vol. 23, pp. 1-16, 2021.
[16] R. Esteller, and G. Vachtsevanos, “A comparison of waveform fractal dimension algorithms,” IEEE Transactions on circuits and systems-Ι: Fundamental theory and applications, Vol. 48, pp. 177-183, 2001.
[17] G. Jaiswal, and R. Paul, “Artificial neural network for ECG classification,” Recent Research in Science and Technology, Vol. 6, pp. 36-38, 2014.
[18] J. Fornari, and J. Pelaze, “Bundle Branch Blocks Classification via ECG using MLP Neural Networks,” Foundations and Applications of Intelligent Systems, Vol. 213, pp. 547-561, 2014.
[19] A. Ebrahimzadeh, and M. Safarnehad, “Classification of ECG signals using Hermite functions and MLP neural networks,” Journal of AI and Data Mining, Vol. 4 (1), pp. 55-65, 2016.
[20] A. Sahedin, Ah. Zakernejad, S. Faridi, M. Javadi, and R. Ebrahimpour, “A Trainable Neural Network Ensemble for ECG Beat Classification,” International Journal of Biomedical and Biological Engineering, Vol. 4 (9), pp. 479-485. 2010.
[21] M. Engin, and S. Demirag, “Fuzzy-hybrid neural network based ECG beat recognition using three different types of feature sets,” Cardiovascular Engineering: An International Journal, Vol. 3 (2), pp. 71-80, 2013.
[22] A. karen, and E. andres, “Classification models for heart disease prediction using feature selection and PCA,” Informatics in Medicine Unlocked, Vol. 19, pp. 1-11, 2021.
[23] R. M. Revadas, “Cardiac Arrhythmia Classification using SVM, KNN and NAIVE BAYES Algorythms,” International Research Journal of Engineering and Technology (IRJET), Vol. 5, pp. 3937-3941, May 2021.
[24] V. R. Elgin Christo, H. Khanna Nehemiah, B. Minu, and A. Kannan, "Correlation-based Ensemble Feature Selection using Bioinspired Algorithms and Classification using Back-propagation Neural Network," Computational and Mathematical Methods in Medicine, Vol. 7, pp. 1-17, Sep 2019.
[25] N. Kohli, and N. K. Verma, “Arrhythmia classification using SVM with selected features,” International Journal of Engineering, Science and Technology, Vol. 3, pp. 122-131, 2011.
[26] L. I. Kuncheva, and J. C. Bezdek, “Decision Templates for Multiple Classifier Fusion: An Experimental Comparison,” Pattern Recognition, Vol. 34, pp. 299-314, 2001.
[27] G. B. Moody, and R. G. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, Vol. 20, pp. 45-50, 2001.
[29] H. D. Abarbanel, and R. Brown, “Lyapunov exponents in chaotic systems: their importance ad their evaluation using observed data,” International journal of modern physics B, Vol. 5, pp. 1347-1375, 1991.
[30] J. P. Eckmann, and S. O. Kamphorst, “Lyapunov exponents from time series,” Physical Review A, Vol. 34, pp. 4971-4979, 1986.
[31] M. Sano, and Y. Sawada, “Measurement of the Lyapunov spectrum from a chaotic time series,” Physical Review Letters, Vol. 55, pp. 1082-1085, 1988.
[32] YS. Hung, and CY Suen, “The behavior – knowledge Space method for Combination of Multi Classifiers,” IEEE Society Conference on Computer Vision and Pattern Recognition., New York., CVPR. 1993, pp. 347-387.
[33] I. Kaur, R. Rajini, and A. Marwaha, “ECG Signal Analysis and Arrhythmia Detection Using Wavelet Transform,” J. Inst. Eng. India Ser. B., Vol. 97, pp. 499-507, Dec. 2016.
[34] J. Pan, and W. J. Tompkins, “A real time QRS detection algorithm,” IEEE Transaction on Biomedical Engineering., Vol. 32, pp. 230-236, 1985.