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ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti

Year 2018, Volume: 22 Issue: 2, 746 - 753, 15.08.2018

İlknur Kayıkçıoğlu Güzin Ulutaş , Fulya Akdeniz Temel Kayıkçıoğlu

Abstract

EKG işaretinde ST değişiminin erken tespit edilmesi myokard enfarktüs önlenmesi açısından oldukça önemlidir. Bu çalışmada ST değişimin erken tespit etmek amacıyla Wigner-Ville dağılımına dayanan bir algoritma geliştirilmiştir. Algoritma MIT-BIH Aritmi ve European ST-T veritabanlarından üretilen büyük bir veride test edilmiştir. MIT-BIH veritabanından V1, V2, V4, V5 derivasyonlarında sağlıklı veya aritmi içeren 111688 R-R aralığı ve European ST-T veritabanından V1, V2, V3, V4, V5 derivasyonlarında 111688 tane ST değişimi olan R-R aralıkları seçilmiştir. Sınıflandırmada performans sonuçları doğruluk, duyarlılık, özgüllük ve pozitif öngörü, sırasıyla  %98,78,  %98,55, %99,0 ve %99,01 olarak bulunmuş olup bu değerler literatürdeki çalışmalara ait değerlerin üstündedir. Ayrıca önerilen algoritmanın hızı tele-tıp sistemleri için oldukça uygundur.

Keywords

Elektrokardiyagram(EKG) ST değişimi; Myokard enfarktüs; Wigner-Ville dağılımı; Teletıp

References

  • [1] Centers for Disease Control and Prevention CDC. 2003. Trends in aging--United States and worldwide. MMWR. Morbidity and mortality weekly report, 52(6), 101.
  • [2] WHO. 2016. Cardiovascular diseasen http://www.who.int/cardiovascular_diseases/en (Erişim Tarihi: 27.12.2016).
  • [3] Xu, M., Wei, S., Qin, X., Zhang, Y., Liu, C. 2015. Rule-Based Method for Morphological Classification of ST Segment in ECG Signals. Journal of Medical and Biological Engineering, 35(6), 816-823.
  • [4] Thygesen, K., Alpert, J. S., White, H. D. 2007. Universal definition of myocardial infarction. Journal of the American College of Cardiology, 50(22), 2173-2195.
  • [5] Roger, V. L., Go, A. S., Lloyd-Jones, D. M., Adams, R. J., Berry, J. D., Brown, T. M., Fox, C. S. 2011. Heart disease and stroke statistics—2011 update a report from the American Heart Association. Circulation, 123(4), e18-e209.
  • [6] Liu, B., Liu, J., Wang, G., Huang, K., Li, F., Zheng, Y., Zhou, F. 2015. A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Computers in biology and medicine, 61, 178-184.
  • [7] Wimmer, N. J., Scirica, B. M., Stone, P. H. 2013. The clinical significance of continuous ECG (ambulatory ECG or Holter) monitoring of the ST-segment to evaluate ischemia: a review. Progress in cardiovascular diseases, 56(2), 195-202.
  • [8] Wootton, R. 2012. Twenty years of telemedicine in chronic disease management–an evidence synthesis. Journal of telemedicine and telecare, 18(4), 211-220.
  • [9] Rabbani, H., Mahjoob, M. P., Farahabadi, E., Farahabadi, A., Dehnavi, A. M. 2011. Ischemia detection by electrocardiogram in wavelet domain using entropy measure. Journal of Research in Medical Sciences, 16(11).
  • [10] Ranjith, P., Baby, P. C., Joseph, P. 2003. ECG analysis using wavelet transform: application to myocardial ischemia detection. ITBM-RBM, 24(1), 44-47.
  • [11] Afsar, F. A., Arif, M., Yang, J. 2008. Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier. Physiological measurement, 29(7), 747.
  • [12] Smrdel, A., Jager, F. 2004. Automated detection of transient ST-segment episodes in 24h electrocardiograms. Medical and Biological Engineering and Computing, 42(3), 303-311.
  • [13] Goletsis, Y., Papaloukas, C., Fotiadis, D. I., Likas, A., Michalis, L. K. 2004. Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis. IEEE transactions on Biomedical Engineering, 51(10), 1717-1725.
  • [14] Andreao, R. V., Dorizzi, B., Boudy, J., Mota, J. C. M. 2004. ST-segment analysis using hidden Markov Model beat segmentation: application to ischemia detection. In Computers in Cardiology, 2004 (pp. 381-384). IEEE.
  • [15] Correa, R., Arini, P. D., Correa, L. S., Valentinuzzi, M., Laciar, E. 2014. Novel technique for ST-T interval characterization in patients with acute myocardial ischemia. Computers in biology and medicine, 50, 49-55.
  • [16] Chang, P. C., Lin, J. J., Hsieh, J. C., Weng, J. 2012. Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Applied Soft Computing, 12(10), 3165-3175.
  • [17] Exarchos, T. P., Papaloukas, C., Fotiadis, D. I., Michalis, L. K. 2006. An association rule mining-based methodology for automated detection of ischemic ECG beats. IEEE Transactions on Biomedical Engineering, 53(8), 1531-1540.
  • [18] Dranca, L., Goni, A., Illarramendi, A. 2009. Real-time detection of transient cardiac ischemic episodes from ECG signals. Physiological measurement, 30(9), 983.
  • [19] Tang, X., Xia, L., Liu, W., Peng, Y., Gao, T., Zeng, Y. 2012. An approach to determine myocardial ischemia by hidden Markov models. Computer methods in biomechanics and biomedical engineering, 15(10), 1065-1070.
  • [20] Al-Fahoum, A., Al-Fraihat, A., Al-Araida, A. 2014. Detection of cardiac ischaemia using bispectral analysis approach. Journal of medical engineering & technology, 38(6), 311-316.
  • [21] Papaloukas, C., Fotiadis, D. I., Liavas, A. P., Likas, A., Michalis, L. K. 2001. A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms. Medical and Biological Engineering and Computing, 39(1), 105-112.
  • [22] Kumar, A., Singh, M. 2016. Ischemia detection using Isoelectric Energy Func-tion. Computers in biology and medicine, 68, 76-83.
  • [23] Physionet. 2016. ECG Database. http://physionet.org/physiobank/database/#ecg (Erişim Tarihi: 27.12.2016).
  • [24] Kayıkçıoğlu, İ., Akdeniz, F., Kayıkçıoğlu, T. 2016. Wigner-Ville distribution based ECG arrhythmia detection for telemedicine applications. In Signal Processing and Communication Application Conference (SIU), 2016 24th (pp. 2045-2048). IEEE.
  • [25] Akdeniz, F., Kayıkçıoğlu, İ., Kaya, İ., Kayıkçıoğlu, T. 2016. Using Wigner-Ville distribution in ECG arrhythmia detection for telemedicine applications. In Telecommunications and Signal Processing (TSP), 2016 39th International Conference on (pp. 409-412). IEEE.
  • [26] Cohen, L. 1995. Time-Frequency Analysis: Theory and Applications, Prentice-Hall, Inc.
  • [27] Brown, G. 2011. Ensemble learning, Encyclopedia of Machine Learning, Springer US,(2011) 312-320.
  • [28] Rokach, L. 2010. Ensemble-based classifiers, Artificial Intelligence Review, 33,1 (2010)1-39.
  • [29] Breiman, L. 2001. Random forests, Machine learning, 45,1 (2001) 5-32.
  • [30] Ho, T. K. 1998. The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20,8 (1998) 832-844.

Year 2018, Volume: 22 Issue: 2, 746 - 753, 15.08.2018

İlknur Kayıkçıoğlu Güzin Ulutaş , Fulya Akdeniz Temel Kayıkçıoğlu

Abstract

References

  • [1] Centers for Disease Control and Prevention CDC. 2003. Trends in aging--United States and worldwide. MMWR. Morbidity and mortality weekly report, 52(6), 101.
  • [2] WHO. 2016. Cardiovascular diseasen http://www.who.int/cardiovascular_diseases/en (Erişim Tarihi: 27.12.2016).
  • [3] Xu, M., Wei, S., Qin, X., Zhang, Y., Liu, C. 2015. Rule-Based Method for Morphological Classification of ST Segment in ECG Signals. Journal of Medical and Biological Engineering, 35(6), 816-823.
  • [4] Thygesen, K., Alpert, J. S., White, H. D. 2007. Universal definition of myocardial infarction. Journal of the American College of Cardiology, 50(22), 2173-2195.
  • [5] Roger, V. L., Go, A. S., Lloyd-Jones, D. M., Adams, R. J., Berry, J. D., Brown, T. M., Fox, C. S. 2011. Heart disease and stroke statistics—2011 update a report from the American Heart Association. Circulation, 123(4), e18-e209.
  • [6] Liu, B., Liu, J., Wang, G., Huang, K., Li, F., Zheng, Y., Zhou, F. 2015. A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Computers in biology and medicine, 61, 178-184.
  • [7] Wimmer, N. J., Scirica, B. M., Stone, P. H. 2013. The clinical significance of continuous ECG (ambulatory ECG or Holter) monitoring of the ST-segment to evaluate ischemia: a review. Progress in cardiovascular diseases, 56(2), 195-202.
  • [8] Wootton, R. 2012. Twenty years of telemedicine in chronic disease management–an evidence synthesis. Journal of telemedicine and telecare, 18(4), 211-220.
  • [9] Rabbani, H., Mahjoob, M. P., Farahabadi, E., Farahabadi, A., Dehnavi, A. M. 2011. Ischemia detection by electrocardiogram in wavelet domain using entropy measure. Journal of Research in Medical Sciences, 16(11).
  • [10] Ranjith, P., Baby, P. C., Joseph, P. 2003. ECG analysis using wavelet transform: application to myocardial ischemia detection. ITBM-RBM, 24(1), 44-47.
  • [11] Afsar, F. A., Arif, M., Yang, J. 2008. Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier. Physiological measurement, 29(7), 747.
  • [12] Smrdel, A., Jager, F. 2004. Automated detection of transient ST-segment episodes in 24h electrocardiograms. Medical and Biological Engineering and Computing, 42(3), 303-311.
  • [13] Goletsis, Y., Papaloukas, C., Fotiadis, D. I., Likas, A., Michalis, L. K. 2004. Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis. IEEE transactions on Biomedical Engineering, 51(10), 1717-1725.
  • [14] Andreao, R. V., Dorizzi, B., Boudy, J., Mota, J. C. M. 2004. ST-segment analysis using hidden Markov Model beat segmentation: application to ischemia detection. In Computers in Cardiology, 2004 (pp. 381-384). IEEE.
  • [15] Correa, R., Arini, P. D., Correa, L. S., Valentinuzzi, M., Laciar, E. 2014. Novel technique for ST-T interval characterization in patients with acute myocardial ischemia. Computers in biology and medicine, 50, 49-55.
  • [16] Chang, P. C., Lin, J. J., Hsieh, J. C., Weng, J. 2012. Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Applied Soft Computing, 12(10), 3165-3175.
  • [17] Exarchos, T. P., Papaloukas, C., Fotiadis, D. I., Michalis, L. K. 2006. An association rule mining-based methodology for automated detection of ischemic ECG beats. IEEE Transactions on Biomedical Engineering, 53(8), 1531-1540.
  • [18] Dranca, L., Goni, A., Illarramendi, A. 2009. Real-time detection of transient cardiac ischemic episodes from ECG signals. Physiological measurement, 30(9), 983.
  • [19] Tang, X., Xia, L., Liu, W., Peng, Y., Gao, T., Zeng, Y. 2012. An approach to determine myocardial ischemia by hidden Markov models. Computer methods in biomechanics and biomedical engineering, 15(10), 1065-1070.
  • [20] Al-Fahoum, A., Al-Fraihat, A., Al-Araida, A. 2014. Detection of cardiac ischaemia using bispectral analysis approach. Journal of medical engineering & technology, 38(6), 311-316.
  • [21] Papaloukas, C., Fotiadis, D. I., Liavas, A. P., Likas, A., Michalis, L. K. 2001. A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms. Medical and Biological Engineering and Computing, 39(1), 105-112.
  • [22] Kumar, A., Singh, M. 2016. Ischemia detection using Isoelectric Energy Func-tion. Computers in biology and medicine, 68, 76-83.
  • [23] Physionet. 2016. ECG Database. http://physionet.org/physiobank/database/#ecg (Erişim Tarihi: 27.12.2016).
  • [24] Kayıkçıoğlu, İ., Akdeniz, F., Kayıkçıoğlu, T. 2016. Wigner-Ville distribution based ECG arrhythmia detection for telemedicine applications. In Signal Processing and Communication Application Conference (SIU), 2016 24th (pp. 2045-2048). IEEE.
  • [25] Akdeniz, F., Kayıkçıoğlu, İ., Kaya, İ., Kayıkçıoğlu, T. 2016. Using Wigner-Ville distribution in ECG arrhythmia detection for telemedicine applications. In Telecommunications and Signal Processing (TSP), 2016 39th International Conference on (pp. 409-412). IEEE.
  • [26] Cohen, L. 1995. Time-Frequency Analysis: Theory and Applications, Prentice-Hall, Inc.
  • [27] Brown, G. 2011. Ensemble learning, Encyclopedia of Machine Learning, Springer US,(2011) 312-320.
  • [28] Rokach, L. 2010. Ensemble-based classifiers, Artificial Intelligence Review, 33,1 (2010)1-39.
  • [29] Breiman, L. 2001. Random forests, Machine learning, 45,1 (2001) 5-32.
  • [30] Ho, T. K. 1998. The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20,8 (1998) 832-844.
There are 30 citations in total.

Details

Journal Section Articles
Authors

İlknur Kayıkçıoğlu This is me

Güzin Ulutaş

Fulya Akdeniz This is me

Temel Kayıkçıoğlu This is me

Publication Date August 15, 2018
Published in Issue Year 2018 Volume: 22 Issue: 2

Cite

APA Kayıkçıoğlu, İ., Ulutaş, G., Akdeniz, F., Kayıkçıoğlu, T. (2018). ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(2), 746-753. https://doi.org/10.19113/sdufbed.40216
AMA Kayıkçıoğlu İ, Ulutaş G, Akdeniz F, Kayıkçıoğlu T. ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti. J. Nat. Appl. Sci. August 2018;22(2):746-753. doi:10.19113/sdufbed.40216
Chicago Kayıkçıoğlu, İlknur, Güzin Ulutaş, Fulya Akdeniz, and Temel Kayıkçıoğlu. “ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22, no. 2 (August 2018): 746-53. https://doi.org/10.19113/sdufbed.40216.
EndNote Kayıkçıoğlu İ, Ulutaş G, Akdeniz F, Kayıkçıoğlu T (August 1, 2018) ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22 2 746–753.
IEEE İ. Kayıkçıoğlu, G. Ulutaş, F. Akdeniz, and T. Kayıkçıoğlu, “ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti”, J. Nat. Appl. Sci., vol. 22, no. 2, pp. 746–753, 2018, doi: 10.19113/sdufbed.40216.
ISNAD Kayıkçıoğlu, İlknur et al. “ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22/2 (August 2018), 746-753. https://doi.org/10.19113/sdufbed.40216.
JAMA Kayıkçıoğlu İ, Ulutaş G, Akdeniz F, Kayıkçıoğlu T. ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti. J. Nat. Appl. Sci. 2018;22:746–753.
MLA Kayıkçıoğlu, İlknur et al. “ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 22, no. 2, 2018, pp. 746-53, doi:10.19113/sdufbed.40216.
Vancouver Kayıkçıoğlu İ, Ulutaş G, Akdeniz F, Kayıkçıoğlu T. ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti. J. Nat. Appl. Sci. 2018;22(2):746-53.
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