Detection of Congestive Heart Failure with Nature-Inspired Optimization and Classifiers Performance Amelioration from ECG Signals

References

• E. J. D. S. Luz, T. M. Nunes, V. H. C. De Albuquerque, J. P. Papa, and D. Menotti, “ECG arrhythmia classification based on optimum-path forest,” Expert. Syst. Appl., Vol. 40, no. 9, pp. 3561–3573, 2013. doi:10.1016/j.eswa.2012.12.063.
   Web of Science ®Google Scholar
• S. Meek, and F. Morris, “ABC of clinical electrocardiography. Introduction. I – leads, rate, rhythm, and cardiac axis,” BMJ (Clinical Research ed.), Vol. 324, no. 733, pp. 415–418, 2002. doi:10.1136/bmj.324.7334.415.
   PubMedGoogle Scholar
• D. Levy, M. G. Larson, R. S. Vasan, W. B. Kannel, and K. K. Ho, “The progression from hypertension to congestive heart failure,” Jama, Vol. 275, no. 20, pp. 1557–1562, 1996.
   PubMed Web of Science ®Google Scholar
• M. A. Allessie, et al., “Pathophysiology and prevention of atrial fibrillation,” Circulation, Vol. 103, no. 5, pp. 769–777, 2001.
   PubMed Web of Science ®Google Scholar
• P. A. Wolf, R. D. Abbott, and W. B. Kannel, “Atrial fibrillation as an independent risk factor for stroke: the Framingham study,” Stroke, Vol. 22, no. 8, pp. 983–988, 1991.
   PubMed Web of Science ®Google Scholar
• S. H. Jambukia, V. K. Dabhi, and H. B. Prajapati. “Classification of ECG signals using machine learning techniques: A survey”, in 2015 International Conference on Advances in Computer Engineering and Applications, pp. 714–721, 2015, doi:10.1109/ICACEA.2015.7164783.
   Google Scholar
• P. M. Rautaharju, P. J. MacInnis, J. W. Warren, H. K. Wolf, P. M. Rykers, and H. P. Calhoun, “Methodology of ECG interpretation in the dalhousie program; NOVACODE ECG classification procedures for clinical trials and population health surveys,” Methods Inf. Med., Vol. 29, no. 04, pp. 362–374, 1990.
   PubMedGoogle Scholar
• T. Mar, S. Zaunseder, J. P. Martínez, M. Llamedo, and R. Poll, ““Optimization of ECG classification by means of feature selection”, IEEE Trans. Biomed. Eng., Vol. 58, no. 8, pp. 2168–2177, Aug. 2011. doi:10.1109/TBME.2011.2113395.
   Web of Science ®Google Scholar
• U. Orhan, “Real-time CHF detection from ECG signals using a novel discretization method,” Comput. Biol. Med., Vol. 43, no. 10, pp. 1556–1562, 2013. doi:10.1016/j.compbiomed.2013.07.015.
   PubMed Web of Science ®Google Scholar
• G. Altan, Y. Kutlu, and N. Allahverdi, “A new approach to early diagnosis of congestive heart failure disease by using Hilbert–Huang transform,” Comput. Methods Programs Biomed., Vol. 137, pp. 23–34, 2016. doi:10.1016/j.cmpb.2016.09.003.
   PubMed Web of Science ®Google Scholar
• R. K. Tripathy, M. R. Paternina, J. G. Arrieta, A. Zamora-Méndez, and G. R. Naik, “Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme,” Comput. Methods Programs Biomed., Vol. 173, pp. 53–65, 2019. doi:10.1016/j.cmpb.2019.03.008.
   PubMed Web of Science ®Google Scholar
• S. Kuntamalla, and L. Ram Gopal Reddy, “Detecting congestive heart failure using heart rate sequential trend analysis plot,” International Journal of Engineering Science and Technology, Vol. 2, no. 12, pp. 7329–7334, 2010.
   Google Scholar
• Y. İşler, and M. Kuntalp, “Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure,” Comput. Biol. Med., Vol. 37, no. 10, pp. 1502–1510, 2010. doi:10.1016/j.compbiomed.2007.01.012.
   Google Scholar
• K. Y.-K. Liao, C.-C. Chiu, and S.-J. Yeh, “A novel approach for classification of congestive heart failure using relatively short-term ECG waveforms and SVM classifier,” in Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2015, pp. 1–4.
   Google Scholar
• A. K. Sangaiah, M. Arumugam, and G.-B. Bian, “An intelligent learning approach for improving ECG signal classification and arrhythmia analysis,” Artif. Intell. Med., Vol. 103, pp. 101788, 2020. doi:10.1016/j.artmed.2019.1008.1788.
   PubMed Web of Science ®Google Scholar
• A. A. Bhurane, M. Sharma, R. San-Tan, and U. Rajendra Acharya, “An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals,” Cogn. Syst. Res., Vol. 55, pp. 82–94, 2019. doi:10.1016/j.cogsys.2018.12.017.
   Web of Science ®Google Scholar
• V. Mondéjar-Guerra, J. Novo, J. Rouco, M. G. Penedo, and M. Ortega, “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers,” Biomed. Signal. Process. Control., Vol. 47, pp. 41–48, 2019. doi:10.1016/j.bspc.2018.08.007.
   Web of Science ®Google Scholar
• S. K. Pandey, R. R. Janghel, and V. Vani, “Patient specific machine learning models for ECG signal classification,” Procedia. Comput. Sci., Vol. 167, pp. 2181–2190, 2020. doi:10.1016/j.procs.2020.03.269.
   Google Scholar
• S. Chen, W. Hua, Z. Li, J. Li, and X. Gao, “Heartbeat classification using projected and dynamic features of ECG signal,” Biomed. Signal. Process. Control., Vol. 31, pp. 165–173, 2017. doi:10.1016/j.bspc.2016.07.010.
   Web of Science ®Google Scholar
• R. Vijaya Arjunan, “ECG signal classification based on statistical features with SVM classification,” Int. J. Adv. Sig. Img. Sci, Vol. 2, no. 1, pp. 5–10, 2016. doi:10.29284/ijasis.2.1.2016.5-10.
   Google Scholar
• D. Ai, J. Yang, Z. Wang, J. Fan, C. Ai, and Y. Wang, “Fast multi-scale feature fusion for ECG heartbeat classification,” EURASIP Journal on Advances in Signal Processing, Vol. 2015, no. 1, pp. 1–11, 2015. doi:10.1186/s13634-015-0231-0.
   Google Scholar
• R. J. Martis, U. Rajendra Acharya, K. M. Mandana, A. K. Ray, and C. Chakraborty, “Cardiac decision making using higher order spectra,” Biomed. Signal. Process. Control., Vol. 8, no. 2, pp. 193–203, 2013. doi:10.1016/j.bspc.2012.08.004.
   Web of Science ®Google Scholar
• M. Boussaa, I. Atouf, M. Atibi, and A. Bennis. “ECG signals classification using MFCC coefficients and ANN classifier”, in 2016 International Conference on Electrical and Information Technologies (ICEIT), 2016, pp. 480–484. doi:10.1109/EITech.2016.7519646.
   Google Scholar
• G. Biagetti, P. Crippa, A. Curzi, S. Orcioni, and C. Turchetti, “A multi-class ECG beat classifier based on the Truncated KLT representation,” in European Modelling Symposium, 2014, pp. 93–98. doi:10.1109/EMS.2014.31.
   Google Scholar
• A. Rizal, and S. Hadiyoso. “ECG signal classification using Hjorth Descriptor”, in 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015, pp. 87–90, doi:10.1109/ICACOMIT.2015.7440181.
   Google Scholar
• X. Qu, C. W. Jian, and G. D. Fei. “ECG signal classification based on BPNN”, in 2011 International Conference on Electric Information and Control Engineering, 2011, pp. 1362–1364. doi:10.1109/ICEICE.2011.5777902.
   Google Scholar
• Z. Ge, Z. Zhu, P. Feng, S. Zhang, J. Wang, and B. Zhou, “ECG-signal classification using SVM with multi-feature,” in 8th International Symposium on Next Generation Electronics (ISNE), pp. 1–3, 2019. doi:10.1109/ISNE.2019.8896430.
   Google Scholar
• W. Li, B. Li, H. L. Guo, Y. X. Fang, F. J. Qiao, and S. W. Zhou, “The ECG signal classification based on ensemble learning of PSO-ELM algorithm,” Neural Netw. World, Vol. 30, no. 4, pp. 265, 2020.
   Web of Science ®Google Scholar
• K. N. Rajesh, and R. Dhuli, “Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine,” Comput. Biol. Med., Vol. 87, pp. 271–284, 2017. doi:10.1016/j.compbiomed.2017.06.006.
   PubMed Web of Science ®Google Scholar
• Physionet Database: https://www.physionet.org/physiobank/database
   Google Scholar
• S. K. Prabhakar, H. Rajaguru, and S. Lee, “A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms,” IEEE Access, Vol. 8, pp. 39875–39897, 2020. doi:10.1109/ACCESS.2020.2975848.
   Google Scholar
• X.-S. Yang. “Flower pollination algorithm for global optimization,” in International conference on unconventional computing and natural computation, Springer, Berlin, Heidelberg, 2012, pp. 240–249.
   Google Scholar
• X.-S. Yang, M. Karamanoglu, and X. He, “Flower pollination algorithm: a novel approach for multi objective optimization,” Eng. Optim., Vol. 46, no. 9, pp. 1222–1237, 2014.
   Web of Science ®Google Scholar
• Rodrigues, Douglas, Xin-She Yang, André Nunes De Souza, and João Paulo Papa, “Binary flower pollination algorithm and its application to feature selection”, in Recent advances in swarm intelligence and evolutionary computation, Springer, Cham, 2015, pp. 85–100.
   Google Scholar
• D. Rodrigues, G. F. Silva, J. P. Papa, A. N. Marana, and X.-S. Yang, “EEG-based person identification through binary flower pollination algorithm,” Expert. Syst. Appl., Vol. 62, pp. 81–90, 2016. doi:10.1016/j.eswa.2016.06.006.
   Web of Science ®Google Scholar
• H. Ismkhan, and K. Zamanifar, “Developing improved greedy crossover to solve symmetric traveling salesman problem,” arXiv Preprint ArXi, Vol. 1209, no. 5339, 2012.
   Google Scholar
• C. M. Preethi, and P. T. Vanathi, “Attribute selection using binary flower pollination algorithm with greedy crossover and ‘one to all’initialisation,” Electron. Lett., Vol. 52, no. 2, pp. 1757–1759, 2016.
   Google Scholar
• M. Abdel-Basset, D. El-Shahat, and I. El-Henawy, “Solving 0–1 knapsack problem by binary flower pollination algorithm,” Neural Comput & Applic, Vol. 31, pp. 5477–5495, 2019. doi:10.1007/s00521-018-3375-7.
   Web of Science ®Google Scholar
• H. Byun, and S.-W. Lee, “Applications of support vector machines for pattern recognition: A survey,” in International workshop on support vector machines, Berlin, Heidelberg: Springer, 2002, pp. 213–236.
   Google Scholar
• X. Fang, Z. Jiang, L. Nan, and L. Chen, “Optimal weighted K-nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment,” IET Commun., Vol. 12, no. 10, pp. 1171–1177, 2018.
   Web of Science ®Google Scholar
• M. T. Van, N. Van Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun., Vol. 11, no. 6, pp. 864–871, 2017.
   Web of Science ®Google Scholar
• K. M. Leung, “Naive Bayesian classifier,” Polytechnic University Department of Computer Science/Finance and Risk Engineering, Vol. 2007, pp. 123–156, 2007.
   Google Scholar
• R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” In Ijcai, Vol. 14, no. 2, pp. 1137–1145, 1995.
   Google Scholar
• S. K. Prabhakar, H. Rajaguru, and S. Lee, “Metaheuristic-based dimensionality reduction and classification analysis of PPG signals for interpreting Cardiovascular disease,” IEEE Access, Vol. 7, pp. 165181–165206, 2019. doi:10.1109/ACCESS.2019.2950220.
   Web of Science ®Google Scholar
• D. Chicco, M. J. Warrens, and G. Jurman, “The Matthews Correlation coefficient (MCC) is more informative than Cohen’s kappa and Brier Score in binary classification assessment,” IEEE Access, Vol. 9, pp. 78368–78381, 2021. doi:10.1109/ACCESS.2021.3084050.
   Web of Science ®Google Scholar