Detecting Abnormal ECG Signals Utilising Wavelet Transform and Standard Deviation
ECG contains very important clinical information about the cardiac activities of the heart. Often the ECG signal needs to be captured for a long period of time in order to identify abnormalities in certain situations. Such signal apart of a large volume often is characterised by low quality due to the noise and other influences. In order to extract features in the ECG signal with time-varying characteristics at first need to be preprocessed with the best parameters. Also, it is useful to identify specific parts of the long lasting signal which have certain abnormalities and to direct the practitioner to those parts of the signal. In this work we present a method based on wavelet transform, standard deviation and variable threshold which achieves 100% accuracy in identifying the ECG signal peaks and heartbeat as well as identifying the standard deviation, providing a quick reference to abnormalities.
[1] Van Alste and T S Schilder. Removal of base-line wander and power-line
interference from the ecg by an efficient fir filter with a reduced number
of taps. IEEE Transactions on Biomedical Engineering, 32(12):1052-1060, 1985.
[2] R.V. Andreao, B. Dorizzi, and J. Boudy. Ecg signal analysis through
hidden markov models. Biomedical Engineering, IEEE Transactions on,
53(8):1541-1549, 2006.
[3] I. Daubechies. Ten Lectures on Wavelets. CBMS-NSF Lecture Notes nr.
61, SIAM, Philadelphia.
[4] Ergun Ercelebi. Electrocardiogram signals de-noising using liftingbased
discrete wavelet transform. Computers in Biology and Medicine, 34(6):479-493, 2004.
[5] Xiao Feng, Yuchi Ming, Jo Jun, Ding Ming-Yue, and Hou Wen-Guang. A research of physical activity-s influence on heart rate using feed forward neural network. Proceedings of the 6th International
Symposium on Neural Networks: Advances in Neural Networks - Part
III, pages 1089-1096, 2009.
[6] D Cuesta Frau and Vladimir Eck. Electrocardiogram baseline removal using wavelet approximations. IEEE Symposium on Computers and Communications, 2000.
[7] Sandeep P Ghael, Akbar M Sayeed, and Richard G Baraniuk. Improved
wavelet denoising via empirical wiener filtering. Proceedings of SPIE,
3169:389-399, 1997.
[8] Fazlul Haque, Hanif Ali, Adnan Kiber, and Tanvir Hasan. Detection
of small variations of ecg features using wavelet. ARPN Journal of
Engineering and Applied Science, 4(6), 2009.
[9] S. S. Joshi and C. V. Ghule. Dwt based beat rate detection in ecg
analysis. Proceedings of the International Conference and Workshop on
Emerging Trends in Technology, pages 765-769, 2010.
[10] H Karel, M Peeters, R Westra, S Moermans, P Haddad, and W Serdijn.
Optimal discrete wavelet design for cardiac signal processing. Conference
Proceedings of the International Conference of IEEE Engineering
in Medicine and Biology Society, 3(c):2769-2772, 2005.
[11] P. Laguna, R. Jane, and P. Caminal. Automatic detection of wave
boundaries in multilead ecg signals: validation with the cse database.
Computers and biomedical research an international journal, 27(1):45-60, 1994.
[12] Ronan Lepage, Jean-marc Boucher, Jean-jacques Blanc, and Jeanchristophe
Cornilly. Ecg segmentation and p-wave feature extraction:
application to patients prone to atrial fibrillation. 2001 Conference
Proceedings of the 23rd Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, 2001. Proc:298–301,
2001.
[13] Cuiwei Li, Chongxun Zheng, and Changfeng Tai. Detection of ecg
characteristic points using wavelet transforms. Biomedical Engineering,
IEEE Transactions on, 42(1):21–28, 1995.
[14] S.Z. Mahamoodabadi, A. Ahmedian, and M.D. Abolhasani. Ecg feature
extraction using daubechies wavelet. Proc of 5th IASTED Inter.Conf. VISUALIZATION,
IMAGING and IMAGE PROCESSING, 7-9, Benidorm,
Spain, 2005.
[15] Riadh Baazaoui Mourad Talbi, Akram Aouinet and Adnane Cherif.
Ecg analysis based on wavelet transform and modulus maxima. IJCSI
International Journal of Computer Science Issues, 9(3):427–435, 2012.
[16] Behzad Mozaffary. Ecg baseline wander elimination using wavelet
packets. Engineering and Technology, 3:22–24, 2005.
[17] N. Nikolaev and A. Gotchev. Denoising of ecg signals using wavelet
shrinkage with time-frequency dependent threshold. Proceedings of
EUSIPCO-98, Greece, pages 2449–2452, 1998.
[18] I. Romero and L. Serrano. Ecg frequency domain features extraction:
a new characteristic for arrhythmias classification. Engineering in
Medicine and Biology Society, 2001. Proceedings of the 23rd Annual
International Conference of the IEEE, 2:2006–2008, 2001.
[19] C. Saritha, V. Sukanya, and Y. Narasimha Murthy. Ecg signal analysis
using wavelet transforms. Bulgarian Journal of Physics, 35(1):67–77,
2008.
[20] Dejan Stantic and Jun Jo. Accent Identification by Clustering and
Scoring Formants. ICISE 2012: International Conference on Information
and Systems Engineering, Zurich, Switzerland, pages 232 – 237, 2012.
[21] Ming Yuchi and Jun Jo. Heart rate prediction based on physical activity
using feedforwad neural network. Convergence and Hybrid Information
Technology, 2008. ICHIT ’08. International Conference on, pages 344–
350, 2008.
[1] Van Alste and T S Schilder. Removal of base-line wander and power-line
interference from the ecg by an efficient fir filter with a reduced number
of taps. IEEE Transactions on Biomedical Engineering, 32(12):1052-1060, 1985.
[2] R.V. Andreao, B. Dorizzi, and J. Boudy. Ecg signal analysis through
hidden markov models. Biomedical Engineering, IEEE Transactions on,
53(8):1541-1549, 2006.
[3] I. Daubechies. Ten Lectures on Wavelets. CBMS-NSF Lecture Notes nr.
61, SIAM, Philadelphia.
[4] Ergun Ercelebi. Electrocardiogram signals de-noising using liftingbased
discrete wavelet transform. Computers in Biology and Medicine, 34(6):479-493, 2004.
[5] Xiao Feng, Yuchi Ming, Jo Jun, Ding Ming-Yue, and Hou Wen-Guang. A research of physical activity-s influence on heart rate using feed forward neural network. Proceedings of the 6th International
Symposium on Neural Networks: Advances in Neural Networks - Part
III, pages 1089-1096, 2009.
[6] D Cuesta Frau and Vladimir Eck. Electrocardiogram baseline removal using wavelet approximations. IEEE Symposium on Computers and Communications, 2000.
[7] Sandeep P Ghael, Akbar M Sayeed, and Richard G Baraniuk. Improved
wavelet denoising via empirical wiener filtering. Proceedings of SPIE,
3169:389-399, 1997.
[8] Fazlul Haque, Hanif Ali, Adnan Kiber, and Tanvir Hasan. Detection
of small variations of ecg features using wavelet. ARPN Journal of
Engineering and Applied Science, 4(6), 2009.
[9] S. S. Joshi and C. V. Ghule. Dwt based beat rate detection in ecg
analysis. Proceedings of the International Conference and Workshop on
Emerging Trends in Technology, pages 765-769, 2010.
[10] H Karel, M Peeters, R Westra, S Moermans, P Haddad, and W Serdijn.
Optimal discrete wavelet design for cardiac signal processing. Conference
Proceedings of the International Conference of IEEE Engineering
in Medicine and Biology Society, 3(c):2769-2772, 2005.
[11] P. Laguna, R. Jane, and P. Caminal. Automatic detection of wave
boundaries in multilead ecg signals: validation with the cse database.
Computers and biomedical research an international journal, 27(1):45-60, 1994.
[12] Ronan Lepage, Jean-marc Boucher, Jean-jacques Blanc, and Jeanchristophe
Cornilly. Ecg segmentation and p-wave feature extraction:
application to patients prone to atrial fibrillation. 2001 Conference
Proceedings of the 23rd Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, 2001. Proc:298–301,
2001.
[13] Cuiwei Li, Chongxun Zheng, and Changfeng Tai. Detection of ecg
characteristic points using wavelet transforms. Biomedical Engineering,
IEEE Transactions on, 42(1):21–28, 1995.
[14] S.Z. Mahamoodabadi, A. Ahmedian, and M.D. Abolhasani. Ecg feature
extraction using daubechies wavelet. Proc of 5th IASTED Inter.Conf. VISUALIZATION,
IMAGING and IMAGE PROCESSING, 7-9, Benidorm,
Spain, 2005.
[15] Riadh Baazaoui Mourad Talbi, Akram Aouinet and Adnane Cherif.
Ecg analysis based on wavelet transform and modulus maxima. IJCSI
International Journal of Computer Science Issues, 9(3):427–435, 2012.
[16] Behzad Mozaffary. Ecg baseline wander elimination using wavelet
packets. Engineering and Technology, 3:22–24, 2005.
[17] N. Nikolaev and A. Gotchev. Denoising of ecg signals using wavelet
shrinkage with time-frequency dependent threshold. Proceedings of
EUSIPCO-98, Greece, pages 2449–2452, 1998.
[18] I. Romero and L. Serrano. Ecg frequency domain features extraction:
a new characteristic for arrhythmias classification. Engineering in
Medicine and Biology Society, 2001. Proceedings of the 23rd Annual
International Conference of the IEEE, 2:2006–2008, 2001.
[19] C. Saritha, V. Sukanya, and Y. Narasimha Murthy. Ecg signal analysis
using wavelet transforms. Bulgarian Journal of Physics, 35(1):67–77,
2008.
[20] Dejan Stantic and Jun Jo. Accent Identification by Clustering and
Scoring Formants. ICISE 2012: International Conference on Information
and Systems Engineering, Zurich, Switzerland, pages 232 – 237, 2012.
[21] Ming Yuchi and Jun Jo. Heart rate prediction based on physical activity
using feedforwad neural network. Convergence and Hybrid Information
Technology, 2008. ICHIT ’08. International Conference on, pages 344–
350, 2008.
@article{"International Journal of Medical, Medicine and Health Sciences:63029", author = "Dejan Stantic and Jun Jo", title = "Detecting Abnormal ECG Signals Utilising Wavelet Transform and Standard Deviation", abstract = "ECG contains very important clinical information about the cardiac activities of the heart. Often the ECG signal needs to be captured for a long period of time in order to identify abnormalities in certain situations. Such signal apart of a large volume often is characterised by low quality due to the noise and other influences. In order to extract features in the ECG signal with time-varying characteristics at first need to be preprocessed with the best parameters. Also, it is useful to identify specific parts of the long lasting signal which have certain abnormalities and to direct the practitioner to those parts of the signal. In this work we present a method based on wavelet transform, standard deviation and variable threshold which achieves 100% accuracy in identifying the ECG signal peaks and heartbeat as well as identifying the standard deviation, providing a quick reference to abnormalities.
", keywords = "Electrocardiogram-ECG, Arrhythmia, Signal Processing, Wavelet Transform, Standard Deviation", volume = "6", number = "11", pages = "609-7", }
