CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm

by Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 27
Year of Publication: 2018
Authors: Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi
10.5120/ijca2018916539

Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi . Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm. International Journal of Computer Applications. 179, 27 ( Mar 2018), 1-8. DOI=10.5120/ijca2018916539

@article{ 10.5120/ijca2018916539,
author = { Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi },
title = { Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 27 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number27/29125-2018916539/ },
doi = { 10.5120/ijca2018916539 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:36.424490+05:30
%A Padmavathi Kora
%A Ch. Usha Kumari
%A K. Meenakshi
%T Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 27
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Atrial Fibrillation (AF) is a common type of heart abnormality. During the AF electrical discharges in the atrium are rapid that results in irregular heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a Wavelet Coherence (WTC) technique for ECG signal analysis. The WTC measures the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Firefly algorithm (FFA). The optimized features from the FFA are given as the input to the Levenberg Marquardt Neural Network (LM NN) classifier. From the literature it is observed that the performance of the classifier is improved with the help of the optimized (reduced) features.

References
  1. Banerjee, S., Mitra, M. (2014). Application of cross wavelet transform for ecg pattern analysis and classification, IEEE transcations on Instrumentation and Measurement, 63, 2, 326–333.
  2. Chatterjee, H., Gupta, R., Mitra, M. (2011). A statistical approach for determination of time plane features from digitized ecg. Computers in biology and medicine, 41, 5, 278-284. Grinsted, A., Moore, J. C.,Jevrejeva, S.(2004). ‘Application of the cross wavelet transform and wavelet coherence to geophysical time series’.Nonlinear processes in geophysics, Vol.11 No.5/6, pp.561-566.
  3. Pal, S. K., Rai, C. S., and Singh, A. P. "Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems". International Journal of Intelligent Systems and Applications (IJISA), 4(10), 50, 2002.
  4. J. Kennedy, R. C. Eberhart, "Particle swarm optimization", IEEE International Conference on Neural Networks, Piscataway, NJ., pp.942-1948, 1995.
  5. Dorigo, Marco, and Thomas Stutzle. "The ant colony optimization metaheuristic: Algorithms, applications, and advances." Handbook of metaheuristics. Springer US, 2003. pp.250-285.
  6. Ibn Ibrahimy, M., Ahsan, R., Khalifa, O.(2013). ‘Design and optimization of levenberg-marquardt based neural network classifier for emg signals to identify hand motions’.Measurement Science Review, Vol.13,No.3, pp.142-151.
  7. Mitra, M., Samanta, R. (2013). ‘Cardiac arrhythmia classification using neural networks with selected features’. Procedia Technology, Vol.10 ,pp. 76-84.
  8. Mohebbi, M.,Ghassemian, H. (2008).‘Detection of atrial fibrillation episodes using svm’. In Engineering in medicine and biology society,30th annual international conference of the ieee,pp. 177-180.
  9. 2014]Moody Moody, G. B., Mark, R. G. (1983). ‘A new method for detecting atrial fibrillation using rr intervals’. Computers in Cardiology,Vol.10, pp. 227-230.
  10. Yang, X. S. (2011). Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation, 3(5), 267-274.
  11. Sapna, S., Tamilarasi, A., Kumar, M. P. (2012). ‘Backpropagation learning algorithm based on levenberg marquardt algorithm’.CS and IT-CSCP,pp. 393-398.
  12. Schamroth,L.(1964). An introduction to electrocardiography. Academic Medicine,Vol.39,No.10,
  13. Singh, G., Singh, C.(2012). ’Estimation of coherence between ecg signal and eeg signal at different heart rates and respiratory rates’.International Journal of Engineering Innovative Technologies,Vol.1 ,No.2 ,pp.159- 163.
  14. Tateno, K., Glass, L.(2000). ‘A method for detection of atrial fibrillation using rr intervals’. In Computers in cardiology 2000,pp. 391-394.
  15. Welch, P. D. (1967). ‘The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms’. IEEE Transactions on audio and electroacoustics, Vol.15,No.2, pp.70-73.
  16. Weng, B., Wang, J. J., Michaud, F.,BlancoVelasco, M. (2008). ‘Atrial fibrillation detection using stationary wavelet transform analysis’. In Engineering in medicine and biology society, embs 2008.30th annual international conference of the ieee, pp.1128-1131.
  17. J. G. Proakis(2001), ‘Digital signal processing: principles algorithms and applications’,Pearson Education India.
  18. Parvaresh, Sanaz, and Ahmad Ayatollahi. "Automatic atrial fibrillation detection using autoregressive modeling." 2011 International Conference on Biomedical Engineering and Technology. 2011.
  19. Lee, Jinseok, et al. "Atrial fibrillation detection using an iPhone 4S." Biomedical Engineering, IEEE Transactions on 60.1 (2013): 203-206.
  20. Tateno, K., and L. Glass. "Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ÎTRR intervals." Medical and Biological Engineering and Computing 39.6 (2001): 664- 671.
  21. Zhou, Xiaolin, et al. "Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy." Biomedical engineering online 13.1 (2014): 18.
  22. Kora, P., and Kalva, S. R. (2015). Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block. SpringerPlus, 4(1), 481.
  23. Kennedy, J. (2010). Particle swarm optimization. In Encyclopedia of Machine Learning (pp. 760-766). Springer US.
  24. Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. Control Systems, IEEE, 22(3), 52-67.
  25. Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., and Winfield, A. (Eds.). (2008). Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008, Proceedings (Vol. 5217). Springer.
  26. Mishra, A., Agarwal, C., Sharma, A., and Bedi, P. (2014). Optimized gray-scale image watermarking using DWTâASSVD and Firefly Algorithm. Expert Systems with Applications, 41(17), 7858-7867.
  27. Nasiri, J., Naghibzadeh, M., Yazdi, H. S., and Naghibzadeh, B. (2009, November). ECG arrhythmia classification with support vector machines and genetic algorithm. In Computer Modeling and Simulation, 2009. EMS’09. Third UKSim European Symposium on (pp. 187-192). IEEE.
  28. Mishra, A., Agarwal, C., Sharma, A., and Bedi, P. (2014). Optimized gray-scale image watermarking using DWTâASSVD and Firefly Algorithm. Expert Systems with Applications, 41(17), 7858-7867.
  29. Sahu, R. K., Panda, S., and Padhan, S. (2015). A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. International Journal of Electrical Power and Energy Systems, 64, 9-23.
  30. Yang, X. S., and Deb, S. (2009, December). Cuckoo search via Levy flights. In Nature and Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.
  31. Spilka, J., Chudacek, V., Kuzilek, J., Lhotska, L., and Hanuliak, M. (2010, September). Detection of inferior myocardial infarction: A comparison of various decision systems and learning algorithms. In Computing in Cardiology, 2010 (pp. 273-276). IEEE.
  32. Sun, L., Lu, Y., Yang, K., and Li, S. (2012). ECG analysis using multiple instance learning for myocardial infarction detection. Biomedical Engineering, IEEE Transactions on, 59(12), 3348-3356.
  33. Fister, I. (2013). A comprehensive review of bat algorithms and their hybridization (Doctoral dissertation, Masters thesis, University of Maribor, Slovenia).
Index Terms

Computer Science
Information Sciences

Keywords

ECG Atrial Fibrillation Wavelet Coherence Firefly algorithm.

Powered by PhDFocusTM