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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

Yıl 2018, Cilt: 8 Sayı: 2, 178 - 182, 30.11.2018

Öz



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.




Kaynakça

  • 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 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 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 Guyon, I., & Elisseeff, A. (2006). 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 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 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 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 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. 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). 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 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 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 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 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 Yılmaz, Z., & Bozkurt, M. R. (2013). Ayrık Dalgacık Dönüşümü Kullanarak Aritmilere Ait Özniteliklerin Çıkarılması, 23–26. 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).

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

Yıl 2018, Cilt: 8 Sayı: 2, 178 - 182, 30.11.2018

Öz



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.




Kaynakça

  • 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 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 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 Guyon, I., & Elisseeff, A. (2006). 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 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 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 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 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. 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). 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 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 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 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 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 Yılmaz, Z., & Bozkurt, M. R. (2013). Ayrık Dalgacık Dönüşümü Kullanarak Aritmilere Ait Özniteliklerin Çıkarılması, 23–26. 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).
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Makbule Hilal Mütevelli

Semih Ergin

Yayımlanma Tarihi 30 Kasım 2018
Gönderilme Tarihi 15 Ağustos 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 2

Kaynak Göster

APA 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), 8(2), 178-182.