Research Article
BibTex RIS Cite

Ateş Böceği Algoritmasını Kullanarak Kardiyak Aritmi Teşhisi

Year 2018, Volume: 21 Issue: 3, 226 - 234, 23.10.2018

Yücel Koçyiğit , Selim Dilmaç

https://doi.org/10.17780/ksujes.435734

Abstract

Elektrokardiyografik
sinyallerdeki aritmileri otomatik olarak teşhis etmek ve kalp atım tipini
sınıflandırma için yeni yöntemler geliştirilmektedir. Bu çalışmada, veri
kümelemesi için Ateş böceği(AB) ve Bulanık C-Ortalama (BCO) algoritmalarını
kullanarak K-En Yakın Komşuluk (K-EYK) yöntemiyle EKG aritmilerinin
sınıflandırmasını gerçekleştirdik. Ateş böceği algoritmasının sonuçları,
Bulanık C-Ortalama algoritması başarı sonuçları ile karşılaştırılmıştır. EKG
verileri MITBIH veri tabanından elde edilmiştir. Ateş böceği  ve Bulanık C-Ortalama algoritmalarını
kullanarak, sınıflandırma doğruluk oranı sırasıyla %99,47 ve %99,54 olarak
bulunmuştur. 

Keywords

Ateş böceği algoritması Bulanık C-Ortalama Kardiyak Aritmi

References

  • Bezdek, J. C. (1981). Objective Function Clustering. In Pattern recognition with fuzzy objective function algorithms (pp. 43-93). Springer, Boston, MA.
  • Chen, S., Hua, W., Li, Z., Li, J., & Gao, X. (2017). Heartbeat classification using projected and dynamic features of ECG signal. Biomedical Signal Processing and Control, 31, 165-173.
  • De Albuquerque, V. H. C., Nunes, T. M., Pereira, D. R., Luz, E. J. D. S., Menotti, D., Papa, J. P., & Tavares, J. M. R. (2018). Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Computing and Applications, 29(3), 679-693.
  • De Falco, I., Della Cioppa, A., & Tarantino, E. (2007). Facing classification problems with particle swarm optimization. Applied Soft Computing, 7(3), 652-658.
  • Dilmac, S., & Korurek, M. (2015). ECG heart beat classification method based on modified ABC algorithm. Applied Soft Computing, 36, 641-655.
  • Doğan, B., & Korürek, M. (2012). A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Applied Soft Computing, 12(11), 3442-3451.
  • Dorigo, M., & Stützle, T. (2004). Ant colony optimization. MIT Press, USA.
  • Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., & Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer methods and programs in biomedicine, 127, 52-63.
  • Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.Jung, W. H., & Lee, S. G. (2017). An Arrhythmia Classification Method in Utilizing the Weighted KNN and the Fitness Rule. IRBM, 38(3), 138-148.
  • Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied soft computing, 11(1), 652-657.
  • Karlιk, B., Koçyiğit, Y., & Korürek, M. (2009). Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network. Expert Systems, 26(1), 49-59.
  • Koçyiğit, Y. (2016). Heart sound signal classification using fast independent component analysis. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2949-2960.
  • Korürek, M., & Nizam, A. (2008). A new arrhythmia clustering technique based on Ant Colony Optimization. Journal of Biomedical Informatics, 41(6), 874-881.
  • Korürek, M., & Nizam, A. (2010). Clustering MIT–BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients. Digital Signal Processing, 20(4), 1050-1060.
  • Kutlu, Y., & Kuntalp, D. (2011). A multi-stage automatic arrhythmia recognition and classification system. Computers in Biology and Medicine, 41(1), 37-45.
  • Li, H., Yuan, D., Ma, X., Cui, D., & Cao, L. (2017). Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Scientific reports, 7, 41011
  • Martis, R. J., Acharya, U. R., Lim, C. M., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2013). Application of higher order cumulant features for cardiac health diagnosis using ECG signals. International journal of neural systems, 23(04), 1350014.
  • Moody, G. B., & Mark, R. (1992). MIT-BIH arrhythmia database directory. MITBIH Database Distribution, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. Available on the World Wide Web at:(http://www. physionet. org/physiobank/database/html/mitdbdir/mitdbdir. htm)(last date visited: Jul. 23, 2008).
  • Selim, S. Z., & Ismail, M. A. (1984). K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on pattern analysis and machine intelligence, (1), 81-87.
  • Senthilnath, J., Omkar, S. N., & Mani, V. (2011). Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 1(3), 164-171.
  • Suganya, R., & Shanthi, R. (2012). Fuzzy c-means algorithm-a review. International Journal of Scientific and Research Publications, 2(11), 1.
  • Yan, X., Zhu, Y., Zou, W., & Wang, L. (2012). A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing, 97, 241-250.
  • Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
  • Yeh, Y. C., Chiou, C. W., & Lin, H. J. (2012). Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Systems with Applications, 39(1), 1000-1010.
  • Zhang, C., Ouyang, D., & Ning, J. (2010). An artificial bee colony approach for clustering. Expert Systems with Applications, 37(7), 4761-4767.
  • Zhang, J., & Shen, L. (2014). An improved fuzzy c-means clustering algorithm based on shadowed sets and PSO. Computational intelligence and neuroscience, 2014, 22.

Year 2018, Volume: 21 Issue: 3, 226 - 234, 23.10.2018

Yücel Koçyiğit , Selim Dilmaç

https://doi.org/10.17780/ksujes.435734

Abstract

References

  • Bezdek, J. C. (1981). Objective Function Clustering. In Pattern recognition with fuzzy objective function algorithms (pp. 43-93). Springer, Boston, MA.
  • Chen, S., Hua, W., Li, Z., Li, J., & Gao, X. (2017). Heartbeat classification using projected and dynamic features of ECG signal. Biomedical Signal Processing and Control, 31, 165-173.
  • De Albuquerque, V. H. C., Nunes, T. M., Pereira, D. R., Luz, E. J. D. S., Menotti, D., Papa, J. P., & Tavares, J. M. R. (2018). Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Computing and Applications, 29(3), 679-693.
  • De Falco, I., Della Cioppa, A., & Tarantino, E. (2007). Facing classification problems with particle swarm optimization. Applied Soft Computing, 7(3), 652-658.
  • Dilmac, S., & Korurek, M. (2015). ECG heart beat classification method based on modified ABC algorithm. Applied Soft Computing, 36, 641-655.
  • Doğan, B., & Korürek, M. (2012). A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Applied Soft Computing, 12(11), 3442-3451.
  • Dorigo, M., & Stützle, T. (2004). Ant colony optimization. MIT Press, USA.
  • Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., & Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer methods and programs in biomedicine, 127, 52-63.
  • Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.Jung, W. H., & Lee, S. G. (2017). An Arrhythmia Classification Method in Utilizing the Weighted KNN and the Fitness Rule. IRBM, 38(3), 138-148.
  • Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied soft computing, 11(1), 652-657.
  • Karlιk, B., Koçyiğit, Y., & Korürek, M. (2009). Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network. Expert Systems, 26(1), 49-59.
  • Koçyiğit, Y. (2016). Heart sound signal classification using fast independent component analysis. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2949-2960.
  • Korürek, M., & Nizam, A. (2008). A new arrhythmia clustering technique based on Ant Colony Optimization. Journal of Biomedical Informatics, 41(6), 874-881.
  • Korürek, M., & Nizam, A. (2010). Clustering MIT–BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients. Digital Signal Processing, 20(4), 1050-1060.
  • Kutlu, Y., & Kuntalp, D. (2011). A multi-stage automatic arrhythmia recognition and classification system. Computers in Biology and Medicine, 41(1), 37-45.
  • Li, H., Yuan, D., Ma, X., Cui, D., & Cao, L. (2017). Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Scientific reports, 7, 41011
  • Martis, R. J., Acharya, U. R., Lim, C. M., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2013). Application of higher order cumulant features for cardiac health diagnosis using ECG signals. International journal of neural systems, 23(04), 1350014.
  • Moody, G. B., & Mark, R. (1992). MIT-BIH arrhythmia database directory. MITBIH Database Distribution, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. Available on the World Wide Web at:(http://www. physionet. org/physiobank/database/html/mitdbdir/mitdbdir. htm)(last date visited: Jul. 23, 2008).
  • Selim, S. Z., & Ismail, M. A. (1984). K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on pattern analysis and machine intelligence, (1), 81-87.
  • Senthilnath, J., Omkar, S. N., & Mani, V. (2011). Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 1(3), 164-171.
  • Suganya, R., & Shanthi, R. (2012). Fuzzy c-means algorithm-a review. International Journal of Scientific and Research Publications, 2(11), 1.
  • Yan, X., Zhu, Y., Zou, W., & Wang, L. (2012). A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing, 97, 241-250.
  • Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
  • Yeh, Y. C., Chiou, C. W., & Lin, H. J. (2012). Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Systems with Applications, 39(1), 1000-1010.
  • Zhang, C., Ouyang, D., & Ning, J. (2010). An artificial bee colony approach for clustering. Expert Systems with Applications, 37(7), 4761-4767.
  • Zhang, J., & Shen, L. (2014). An improved fuzzy c-means clustering algorithm based on shadowed sets and PSO. Computational intelligence and neuroscience, 2014, 22.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Yücel Koçyiğit 0000-0003-1785-198X

Selim Dilmaç

Publication Date October 23, 2018
Submission Date June 22, 2018
Published in Issue Year 2018 Volume: 21 Issue: 3

Cite

APA Koçyiğit, Y., & Dilmaç, S. (2018). Ateş Böceği Algoritmasını Kullanarak Kardiyak Aritmi Teşhisi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 21(3), 226-234. https://doi.org/10.17780/ksujes.435734