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Dergiler / Ejovoc (Electronic Journal of Vocational Colleges) / 2018 / Cilt: 8 Sayı: 2 Özel Sayı

THE USAGE OF STATISTICAL FEATURES IN THE APPROXIMATION COMPONENTS OF WAVELET DECOMPOSITION FOR ECG CLASSIFICATION: A CASE STUDY FOR STANDING, WALKING AND SINGLE JUMP CONDITIONS

Makbule Hilal Mütevelli
Semih Ergin
Sayfa
178–182
Yayın Tarihi
2018-11-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

ECG signals are classified by the Naive Bayes method after the required features are extracted.

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Amaç & Hipotez:Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normal Tablo"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The purpose of this study is to classify electrocardiogram (ECG) signals with a high accuracy rate.
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Temel Bulgular & Veri:The ECG signals used are obtained from the Physiobank archive.
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Sonuç & Katkı:ECG signals are classified by the Naive Bayes method after the required features are extracted.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:ECG · statistical feature extraction · discrete wavelet transform

Abstract

Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normal Tablo"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The purpose of this study is to classify electrocardiogram (ECG) signals with a high accuracy rate. The ECG signals used are obtained from the Physiobank archive. These signals are preprocessed to remove noise. Features with distinctiveness in classification are obtained both in the time domain and the frequency domain. The Discrete Wavelet Transform method is used for feature extraction in frequency domain. ECG signals are classified by the Naive Bayes method after the required features are extracted.

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Bu Makaleye Atıf Yap

Mütevelli, M. H., Ergin, S. (2018). THE USAGE OF STATISTICAL FEATURES IN THE APPROXIMATION COMPONENTS OF WAVELET DECOMPOSITION FOR ECG CLASSIFICATION: A CASE STUDY FOR STANDING, WALKING AND SINGLE JUMP CONDITIONS. Ejovoc (Electronic Journal of Vocational Colleges), Cilt: 8 Sayı: 2 Özel Sayı, 178–182. https://ajindex.com/articles/1266336

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Kaynakça (34)

  1. C.Bakır. (2015). “ECG Signals Classification with Neighborhood Feature Extraction Method.” In Medical Technologies National Conference (TIPTEKNO), 2015 (pp. 173–176). https://doi.org/10.1109/TIPTEKNO.2015.7374099
  2. Ceylan, R., Özbay, Y., & Karlik, B. (2009). A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications, 36(3 PART 2). https://doi.org/10.1016/j.eswa.2008.08.028
  3. Datian, Y., & Xuemei, O. (1996). Application of wavelet analysis in detection of fetal ECG. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Vol. 3). Elhaj, F. A., Salim, N., Harris, A. R., Tian, 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. https://doi.org/10.1016/j.cmpb.2015.12.024
  4. Guyon, I., & Elisseeff, A. (2006).
  5. Feature Extraction, Foundations and Applications: An introduction to feature extraction. Stud. Fuzziness Soft Comput., 207, 1–25. https://doi.org/10.1007/978-3-540-35488-8_1
  6. Islam, M. K., Haque, A. N. M. M., Tangim, G., Ahammad, T., & Khondokar, M. R. H. (2012). Study and analysis of ECG signal using MATLAB & LABVIEW as effective tools. International Journal of Computer and Electrical Engineering, 4(3), 404–408. https://doi.org/10.7763/IJCEE.2012.V4.522
  7. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El, O., & Maoulainine, F. M. R. (2016). ScienceDirect An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Integrative Medicine Research, 36(3), 499–508. https://doi.org/10.1016/j.bbe.2016.04.001
  8. Joy, R., Acharya, U. R., & Choo, L. (2013). Biomedical Signal Processing and Control Technical note ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform. Biomedical Signal Processing and Control, 8(5), 437–448. https://doi.org/10.1016/j.bspc.2013.01.005
  9. Lei, L., Wang, C., & Liu, X. (2013). Discrete Wavelet Transform Decomposition Level Determination Exploiting Sparseness Measurement. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 7(9), 691–694.
  10. Mahmoodabadi, S. Z., Ahmadian, A., & Abolhasani, M. D. (2005). Ecg feature extraction using daubechies wavelets. In Proceedings of the Fifth IASTED International Conference (Vol. 2, pp. 343–348).
  11. Martens, S. M. M., Rabotti, C., Mischi, M., & Sluijter, R. J. (2007). A robust fetal ECG detection method for abdominal recordings. Physiological Measurement, 28(4), 373–388. https://doi.org/10.1088/0967-3334/28/4/004
  12. Mitra, S., Mitra, M., & Chaudhuri, B. B. (2006). A rough-set-based inference engine for ECG classification. IEEE Transactions on Instrumentation and Measurement, 55(6), 2198–2206. https://doi.org/10.1109/TIM.2006.884279
  13. Poungponsri, S., & Yu, X.-H. (2013). An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing, 117, 206–213. https://doi.org/10.1016/j.neucom.2013.02.010
  14. Saritha, C., Sukanya, V., & Murthy, Y. N. (2008). ECG signal analysis using wavelet transforms. Bulg. J. Phys, 35(1), 68–77. Retrieved from http://bjp-bg.com/papers/bjp2008_1_68-77.pdf%5Cnhttp://www.iiste.org/Journals/index.php/ISDE/article/viewFile/610/499
  15. Yeh, Y. C., & Wang, W. J. (2008). QRS complexes detection for ECG signal: The Difference Operation Method. Computer Methods and Programs in Biomedicine, 91(3), 245–254. https://doi.org/10.1016/j.cmpb.2008.04.006
  16. Yılmaz, Z., & Bozkurt, M. R. (2013). Ayrık Dalgacık Dönüşümü Kullanarak Aritmilere Ait Özniteliklerin Çıkarılması, 23–26.
  17. Zhao, Q., & Zhang, L. (2006). ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. In International Conference on Neural Networks and Brain, 2005. Icnn&b (pp. 1089–1092).
  18. C.Bakır. (2015). “ECG Signals Classification with Neighborhood Feature Extraction Method.” In Medical Technologies National Conference (TIPTEKNO), 2015 (pp. 173–176). https://doi.org/10.1109/TIPTEKNO.2015.7374099
  19. Ceylan, R., Özbay, Y., & Karlik, B. (2009). A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications, 36(3 PART 2). https://doi.org/10.1016/j.eswa.2008.08.028
  20. Datian, Y., & Xuemei, O. (1996). Application of wavelet analysis in detection of fetal ECG. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Vol. 3). Elhaj, F. A., Salim, N., Harris, A. R., Tian, 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. https://doi.org/10.1016/j.cmpb.2015.12.024
  21. Guyon, I., & Elisseeff, A. (2006).
  22. Feature Extraction, Foundations and Applications: An introduction to feature extraction. Stud. Fuzziness Soft Comput., 207, 1–25. https://doi.org/10.1007/978-3-540-35488-8_1
  23. Islam, M. K., Haque, A. N. M. M., Tangim, G., Ahammad, T., & Khondokar, M. R. H. (2012). Study and analysis of ECG signal using MATLAB & LABVIEW as effective tools. International Journal of Computer and Electrical Engineering, 4(3), 404–408. https://doi.org/10.7763/IJCEE.2012.V4.522
  24. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El, O., & Maoulainine, F. M. R. (2016). ScienceDirect An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Integrative Medicine Research, 36(3), 499–508. https://doi.org/10.1016/j.bbe.2016.04.001
  25. Joy, R., Acharya, U. R., & Choo, L. (2013). Biomedical Signal Processing and Control Technical note ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform. Biomedical Signal Processing and Control, 8(5), 437–448. https://doi.org/10.1016/j.bspc.2013.01.005
  26. Lei, L., Wang, C., & Liu, X. (2013). Discrete Wavelet Transform Decomposition Level Determination Exploiting Sparseness Measurement. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 7(9), 691–694.
  27. Mahmoodabadi, S. Z., Ahmadian, A., & Abolhasani, M. D. (2005). Ecg feature extraction using daubechies wavelets. In Proceedings of the Fifth IASTED International Conference (Vol. 2, pp. 343–348).
  28. Martens, S. M. M., Rabotti, C., Mischi, M., & Sluijter, R. J. (2007). A robust fetal ECG detection method for abdominal recordings. Physiological Measurement, 28(4), 373–388. https://doi.org/10.1088/0967-3334/28/4/004
  29. Mitra, S., Mitra, M., & Chaudhuri, B. B. (2006). A rough-set-based inference engine for ECG classification. IEEE Transactions on Instrumentation and Measurement, 55(6), 2198–2206. https://doi.org/10.1109/TIM.2006.884279
  30. Poungponsri, S., & Yu, X.-H. (2013). An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing, 117, 206–213. https://doi.org/10.1016/j.neucom.2013.02.010
  31. Saritha, C., Sukanya, V., & Murthy, Y. N. (2008). ECG signal analysis using wavelet transforms. Bulg. J. Phys, 35(1), 68–77. Retrieved from http://bjp-bg.com/papers/bjp2008_1_68-77.pdf%5Cnhttp://www.iiste.org/Journals/index.php/ISDE/article/viewFile/610/499
  32. Yeh, Y. C., & Wang, W. J. (2008). QRS complexes detection for ECG signal: The Difference Operation Method. Computer Methods and Programs in Biomedicine, 91(3), 245–254. https://doi.org/10.1016/j.cmpb.2008.04.006
  33. Yılmaz, Z., & Bozkurt, M. R. (2013). Ayrık Dalgacık Dönüşümü Kullanarak Aritmilere Ait Özniteliklerin Çıkarılması, 23–26.
  34. Zhao, Q., & Zhang, L. (2006). ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. In International Conference on Neural Networks and Brain, 2005. Icnn&b (pp. 1089–1092).