ST Değişiminin Wigner-Ville Dağılım Esaslı Erken Tespiti

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.

___

  • [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.
Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi-Cover
  • ISSN: 1300-7688
  • Yayın Aralığı: Yılda 3 Sayı
  • Başlangıç: 1995
  • Yayıncı: Süleyman Demirel Üniversitesi