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Arrhythmia Detection Using Empirical Mode Decomposition and Boosted Trees in Electrocardiography Signals

Year 2019, Volume: 9 Issue: 1, 103 - 110, 30.06.2019
https://doi.org/10.31466/kfbd.546569

Abstract

Nowadays, heart diseases
that cause death have become widespread. Electrocardiography is a biomedical
signal commonly used in the diagnosis of these diseases. In this study, a
technique which can be used for detecting arrhythmia as a result of ECG
examination is proposed. In order to detect arrhythmia, Empirical Mode
Decomposition and Singular Value Decomposition were used. Empirical Mode
Decomposition is an appropriate technique for analysis of the stationary,
non-linear series and uses oscillation signals at the local levels. It
separates the signals into oscillation structures called Intrinsic Mode
Functions. Singular Value Decomposition is an algebraic method used to reduce
the size of complex data sets and is used to reduce noise effects. After
reducing the effect of noise and obtaining the appropriate features, the
classification was made by using Boosted Trees. Accuracy, sensitivity, and
specificity values were calculated to evaluate the performance of the
classification.

References

  • Blanco-Velasco, M., Weng, B., & Barner, K. E. (2008). ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in biology and medicine, 38(1), 1-13.
  • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
  • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).
  • Labate, D., La Foresta, F., Occhiuto, G., Morabito, F. C., Lay-Ekuakille, A., & Vergallo, P. (2013). Empirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: A comparison. IEEE Sensors Journal, 13(7), 2666-2674.
  • Lagerlund, T. D., Sharbrough, F. W., & Busacker, N. E. (1997). Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Journal of clinical neurophysiology, 14(1), 73-82.
  • McDonald, A. J., Baumgaertner, A. J. G., Fraser, G. J., George, S. E., & Marsh, S. (2007, March). Empirical Mode Decomposition of the atmospheric wave field. In Annales Geophysicae (Vol. 25, No. 2, pp. 375-384).
  • Pal, S., & Mitra, M. (2012). Empirical mode decomposition based ECG enhancement and QRS detection. Computers in biology and medicine, 42(1), 83-92.
  • Tomak, Ö., & Kayıkçıoğlu, T. (2018). Bagged tree classification of arrhythmia using wavelets for denoising, compression, and feature extraction. Turkish Journal of Electrical Engineering & Computer Sciences, 26(3), 1555-1571.
  • Weng, B., Blanco-Velasco, M., & Barner, K. E. (2006, August). ECG denoising based on the empirical mode decomposition. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1-4). IEEE. (Lagerlund ve ark., 1997). (Tomak ve Kayıkçıoğlu, 2018)

Elektrokardiyografi Sinyallerinde Deneysel Mod Ayrıştırma Ve Geliştirilmiş Karar Ağaçları Kullanarak Aritmi Tespiti

Year 2019, Volume: 9 Issue: 1, 103 - 110, 30.06.2019
https://doi.org/10.31466/kfbd.546569

Abstract

Günümüzde ölüme neden olan kalp
hastalıkları yaygınlaşmıştır. Elektrokardiyografi bu hastalıkların teşhis aşamasında
sıkça kullanılan biyomedikal bir sinyaldir. Bu çalışmada, EKG incelemesi
sonucunda aritmiyi saptamada kullanılabilecek bir teknik önerilmiştir. Aritmiyi
tespit için, Deneysel Mod Ayrıştırma ve de Tekil Değerlere Ayrıştırma
kullanıldı. Deneysel Mod Ayrıştırma durağan, doğrusal olmayan serileri analiz
için uygun bir tekniktir ve yerel düzeyindeki salınım sinyallerini kullanır.
Sinyalleri, İç Mod Fonksiyonları adındaki salınım yapılarına ayrıştırır. Tekil
Değerlere Ayrıştırma ise karmaşık veri setlerinin boyutlarını küçültülmede kullanılan
bir cebirsel yöntemdir ve gürültü etkilerini azaltmada kullanılmıştır. Gürültünün
etkisinin azaltılmasından ve uygun öznitelliklerin elde edilmesinden sonra,
Sınıflandırma, Geliştirilmiş Karar Ağaçları kullanılarak yapıldı. Sınıflandırmanın
performansını değerlendirmede doğruluk, duyarlılık ve özgünlük değerleri
hesaplandı. 

References

  • Blanco-Velasco, M., Weng, B., & Barner, K. E. (2008). ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in biology and medicine, 38(1), 1-13.
  • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
  • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).
  • Labate, D., La Foresta, F., Occhiuto, G., Morabito, F. C., Lay-Ekuakille, A., & Vergallo, P. (2013). Empirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: A comparison. IEEE Sensors Journal, 13(7), 2666-2674.
  • Lagerlund, T. D., Sharbrough, F. W., & Busacker, N. E. (1997). Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Journal of clinical neurophysiology, 14(1), 73-82.
  • McDonald, A. J., Baumgaertner, A. J. G., Fraser, G. J., George, S. E., & Marsh, S. (2007, March). Empirical Mode Decomposition of the atmospheric wave field. In Annales Geophysicae (Vol. 25, No. 2, pp. 375-384).
  • Pal, S., & Mitra, M. (2012). Empirical mode decomposition based ECG enhancement and QRS detection. Computers in biology and medicine, 42(1), 83-92.
  • Tomak, Ö., & Kayıkçıoğlu, T. (2018). Bagged tree classification of arrhythmia using wavelets for denoising, compression, and feature extraction. Turkish Journal of Electrical Engineering & Computer Sciences, 26(3), 1555-1571.
  • Weng, B., Blanco-Velasco, M., & Barner, K. E. (2006, August). ECG denoising based on the empirical mode decomposition. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1-4). IEEE. (Lagerlund ve ark., 1997). (Tomak ve Kayıkçıoğlu, 2018)
There are 9 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Özgür Tomak 0000-0003-2993-6913

Publication Date June 30, 2019
Published in Issue Year 2019 Volume: 9 Issue: 1

Cite

APA Tomak, Ö. (2019). Elektrokardiyografi Sinyallerinde Deneysel Mod Ayrıştırma Ve Geliştirilmiş Karar Ağaçları Kullanarak Aritmi Tespiti. Karadeniz Fen Bilimleri Dergisi, 9(1), 103-110. https://doi.org/10.31466/kfbd.546569