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Heart disease detection using hybrid of bacterial foraging and particle swarm optimization

  • Padmavathi KoraEmail author
  • Ajith Abraham
  • K Meenakshi
Original Paper
  • 11 Downloads

Abstract

Bacterial-foraging-optimization (BFO) has newly raised and one of the most useful nature inspired optimization algorithm for real parametric optimization. During the process of random walk, the BFO algorithm makes search in the random direction, which increases delay. To overcome the delay in reaching the global optimum and also to boost up the performance of BFO, we proposed an algorithm by mixing the features of BFO and particle swarm optimization (PSO) for detecting the abnormal cardiac beat. Computer simulations illustrate the usefulness of the developed approach compared to the basic versions of BFO and PSO. The main aim of the research is to develop new modifications of BFO and its combination with transform technique such as Wavelet Transform and machine learning method, support vector machines (SVMs) to test their performances in the detection of cardiac arrhythmia. Modification of BFO focuses for improving its convergence in terms of speed and accuracy. Provided results in this paper show that, for the detection of MI and BBB classes, the BFPSO algorithm with SVM gives 98.9% and 99.3% accuracy on MIT-BIH database by including NSR database also. Moreover, the results demonstrate the effectiveness of the proposed method to improve the detection of cardiac arrhythmia.

Keywords

Electrocardiography (ECG) Bacterial foraging optimization (BFO) Particle swarm optimization (PSO) Support vector machine (SVM) 

Notes

References

  1. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415:190–198CrossRefGoogle Scholar
  2. Angelov P, Kasabov N (2006) Evolving intelligent systems, eIS. IEEE SMC eNewsLetter 15:1–13Google Scholar
  3. Anguluri R, Abraham A, Snasel V (2011) A Hybrid Bacterial foraging-PSO algorithm based tuning of optimal FOPI speed controller. Acta Montan Slovaca 16(1):55Google Scholar
  4. Babaoglu İ, Findik O, Ülker E (2010) A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst Appl 37(4):3177–3183CrossRefGoogle Scholar
  5. Biswas A, Dasgupta S, Das S, Abraham A (2007) Synergy of PSO and bacterial foraging optimization—a comparative study on numerical benchmarks. In: Corchado E, Corchado JM, Abraham A (eds) Innovations in hybrid intelligent systems, vol 44. Springer, Berlin, Heidelberg, pp 255–263CrossRefGoogle Scholar
  6. Ceylan R, Özbay Y (2011) Wavelet neural network for classification of bundle branch blocks. Proc World Congr Eng 2(4):1–5Google Scholar
  7. Das S, Biswas A, Dasgupta S, Abraham A (2009) Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Abraham A, Hassanien AE, Siarry P, Engelbrecht A (eds) Foundations of computational intelligence, vol 3. Springer, Berlin, Heidelberg, pp 23–55Google Scholar
  8. Debbarma S, Saikia LC, Sinha N (2014) Automatic generation control using two degree of freedom fractional order PID controller. Int J Electr Power Energy Syst 58:120–129CrossRefGoogle Scholar
  9. El-Wakeel AS, Ellissy AEEKM, Abdel-hamed AM (2015) A hybrid bacterial foraging-particle swarm optimization technique for optimal tuning of proportional-integral-derivative controller of a permanent magnet brushless DC motor. Electric Power Compon Syst 43(3):309–319CrossRefGoogle Scholar
  10. Engin M (2004) ECG beat classification using neuro-fuzzy network. Pattern Recogn Lett 25(15):1715–1722CrossRefGoogle Scholar
  11. Faust O, Acharya UR, Adeli H, Adeli A (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56–64CrossRefGoogle Scholar
  12. Fong S, Wong R, Vasilakos AV (2016) Accelerated PSO swarm search feature selection for data stream mining big data. IEEE Trans Serv Comput 9(1):33–45Google Scholar
  13. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRefGoogle Scholar
  14. Garcia MP, Montiel O, Castillo O, Sepúlveda R, Melin P (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl Soft Comput 9(3):1102–1110CrossRefGoogle Scholar
  15. Hramov AE, Koronovskii AA, Makarov VA, Pavlov AN, Sitnikova E (2015) Mathematical methods of signal processing in neuroscience. In: Wavelets in neuroscience. Springer, Berlin, Heidelberg, pp 1–13zbMATHGoogle Scholar
  16. Kaveh A, Chung W (2013) Automated classification of coronary atherosclerosis using single lead ECG. In: 2013 IEEE conference on wireless sensor (ICWISE). IEEE, pp 108–113.  https://doi.org/10.1109/ICWISE.2013.6728790
  17. Kim WS, Jin SH, Park YK, Choi HM (2007) A study on development of multi-parametric measure of heart rate variability diagnosing cardiovascular disease. In: World congress on medical physics and biomedical engineering 2006. Springer, Berlin, Heidelberg, pp 3480–3483Google Scholar
  18. Kora P (2017) ECG based myocardial infarction detection using hybrid firefly algorithm. Comput Methods Programs Biomed 152:141–148CrossRefGoogle Scholar
  19. Kora P, Kalva SR (2015) Hybrid bacterial foraging and particle swarm optimization for detecting bundle branch block. SpringerPlus 4(1):481CrossRefGoogle Scholar
  20. Kumar A (2014) Changing trends of cardiovascular risk factors among Indians: a review of emerging risks. Asian Pac J Trop Biomed 4(12):1001–1008CrossRefGoogle Scholar
  21. Kumar KS, Jayabarathi T (2012) Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm. Int J Electr Power Energy Syst 36(1):13–17CrossRefGoogle Scholar
  22. Kumar M, Pachori RB, Acharya UR (2017) Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals. Biomed Signal Process Control 31:301–308CrossRefGoogle Scholar
  23. Lee ZJ, Su SF, Chuang CC, Liu KH (2008) Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl Soft Comput 8(1):55–78CrossRefGoogle Scholar
  24. Lee J, Reyes BA, McManus DD, Maitas O, Chon KH (2013) Atrial fibrillation detection using an iPhone 4S. IEEE Trans Biomed Eng 60(1):203–206CrossRefGoogle Scholar
  25. Lehtinen R, Hoist O, Turjanmaa V, Edenbrandt L, Pahlm O, Malmivuo J (1998) Artificial neural network for the exercise electrocardiographic detection of coronary artery disease. In: Proceedings of the 2nd international conference on bioelectromagnetism (Cat. No. 98TH8269). IEEE, pp 57–58Google Scholar
  26. Lewenstein K (2001) Radial basis function neural network approach for the diagnosis of coronary artery disease based on the standard electrocardiogram exercise test. Med Biol Eng Comput 39(3):362–367CrossRefGoogle Scholar
  27. Majhi R, Panda G, Majhi B, Sahoo G (2009) Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Syst Appl 36(6):10097–10104CrossRefGoogle Scholar
  28. Manasrah AM, Ba Ali H (2018) Workflow scheduling using hybrid ga-pso algorithm in cloud computing. Wirel Commun Mobile Comput.  https://doi.org/10.1155/2018/1934784 CrossRefGoogle Scholar
  29. Moeini R, Babaei M (2017) Constrained improved particle swarm optimization algorithm for optimal operation of large scale reservoir: proposing three approaches. Evol Syst 8(4):287–301CrossRefGoogle Scholar
  30. Ordóñez-De León B, Aceves-Fernandez MA, Fernandez-Fraga SM, Ramos-Arreguín JM, Gorrostieta-Hurtado E (2019) An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10). Evol Syst.  https://doi.org/10.1007/s12530-019-09263-y CrossRefGoogle Scholar
  31. Padmavathi K, Krishna KSR (2014) Myocardial infarction detection using magnitude squared coherence and support vector machine. In: 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), 7-8 November 2014, Greater Noida, India. IEEE, pp 382–385.  https://doi.org/10.1109/MedCom.2014.7006037
  32. Rajasekhar A, Jatoth RK, Abraham A, Snasel V (2011) A novel hybrid ABF-PSO algorithm based tuning of optimal FOPI speed controller for PMSM drive. In: 2011 12th International Carpathian Control Conference (ICCC). IEEE, pp 320–325.  https://doi.org/10.1109/CarpathianCC.2011.5945872
  33. Raju M, Gupta MK, Bhanot N, Sharma VS (2018) A hybrid PSO-BFO evolutionary algorithm for optimization of fused deposition modelling process parameters. J Intell Manuf.  https://doi.org/10.1007/s10845-018-1420-0 CrossRefGoogle Scholar
  34. Roeva O, Fidanova S, Paprzycki M (2016) InterCriteria analysis of ACO and GA hybrid algorithms. In: Fidanova S (ed) Recent advances in computational optimization, Studies in Computational Intelligence, vol 610. Springer, Cham, pp 107–126Google Scholar
  35. Schreck DM, Ng L, Schreck BS, Bosco SF, Allegra JR, Zacharias D, Wortzel JV (1988) Detection of coronary artery disease from the normal resting ECG using nonlinear mathematical transformation. Ann Emerg Med 17(2):132–134CrossRefGoogle Scholar
  36. Shanmugasundaram K, Mohmed ASA, Ruhaiyem NIR, Mizher MAA, Choo AM, Abdullah SNHS, Razi MJM (2019) Hybrid improved bacterial swarm optimization algorithm for hand-based multimodal biometric authentication system. J ICT 18(2):123–141Google Scholar
  37. Sridhar C, Acharya UR, Fujita H, Bairy GM (2016) Automated diagnosis of coronary artery disease using nonlinear features extracted from ECG signals. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 545–549.  https://doi.org/10.1109/SMC.2016.7844296
  38. Tam JH, Ong ZC, Ismail Z, Ang BC, Khoo SY, Li WL (2018) Inverse identification of elastic properties of composite materials using hybrid GA-ACO-PSO algorithm. Inverse Probl Sci Eng 26(10):1432–1463MathSciNetCrossRefGoogle Scholar
  39. Vetterli M, Kovačević J, Goyal VK (2014) Foundations of signal processing. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  40. Xizhi Z (2008) The application of wavelet transform in digital image processing. In: 2008 International Conference on MultiMedia and Information Technology. IEEE, pp 326–329.  https://doi.org/10.1109/MMIT.2008.134
  41. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. arXiv:1003.1409
  42. Younus ZS, Mohamad D, Saba T, Alkawaz MH, Rehman A, Al-Rodhaan M, Al-Dhelaan A (2015) Content-based image retrieval using PSO and k-means clustering algorithm. Arab J Geosci 8(8):6211–6224CrossRefGoogle Scholar
  43. Zhang Y, Jun Y, Wei G, Wu L (2010) Find multi-objective paths in stochastic networks via chaotic immune PSO. Expert Syst Appl 37(3):1911–1919CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia
  2. 2.Machine Intelligence Research LabsWashingtonUSA

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