International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 9, No. 6, 8 Jun. 2017
Cover page and Table of Contents: PDF (size: 1144KB)
The Feature Extraction to Determine the Wave’s Peaks in the Electrocardiogram Graphic Image
Full Text (PDF, 1144KB), PP.1-13
Views: 0 Downloads: 0
Author(s)
Darwan
1,2,*
Sri Hartati
1
Retantyo Wardoyo
1
Budi Yuli Setianto
3
Index Terms
Peak determination, ECG, Pre-Processing, Feature Extraction, Heart Rate, ST-Deviation
Abstract
The electrocardiogram (ECG) will create the characteristic in the form of the wave’s peak pattern. The first peak and the next one in one ECG wave have their own value and names, namely PQRST peaks. The process of feature extraction is very significant to determine the certain pattern. The use of feature extraction will be useful to help to detect certain case, including the determination of PQRST peaks according to the ECG print-out. This study makes a method to determine the ECG peaks (PQRST), the heart rate, and ST-deviation according to the ECG graphic image. The input data is in the form of ECG graphic image which is derived from the ECG 12 lead record. This study employs segmentation method (grayscale and binary), morphology (dilation and erosion), and produce the graphic image which is read as the ECG signal in the pre-processing stage, and use the Pan-Tompkins algorithm for the feature extraction method. The result of the peak determination is validated by cardiologists. The validation shows that the result of up and down deflection computation from the isoelectric of each P, Q, R, S, and T wave has represented the ECG calculation clinically; including the calculation to determine the R-R interval, heart rate, and ST-deviation.
Cite This Paper
Darwan, Sri Hartati, Retantyo Wardoyo, Budi Yuli Setianto,"The Feature Extraction to Determine the Wave’s Peaks in the Electrocardiogram Graphic Image", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.6, pp.1-13, 2017. DOI: 10.5815/ijigsp.2017.06.01
Reference
[1]S. Jadhav, S. Nalbalwar, S., and A. Ghatol, “Feature Elimination Based Random Subspace Ensembles Learning For ECG Arrhythmia Diagnosis”, Springer-Verlag Berlin Heidelberg, Soft Comput, DOI 10.1007/s00500-013-1079-6, pp. 1-9, 2013.
[2]R. K. Pramuyanti, Klasifikasi Pola Isyarat EKG Menggunakan Logika Fuzzy, Tesis, Program Studi Teknik Elektro Universitas Gajah Mada, Yogyakarta, 2004.
[3]D. Febrianty, R. A. Dewanto, and Aradea, “Analisis Jaringan Syaraf Tiruan PROP Untuk Mengenali Pola Elektrokardiografi Dalam Mendeteksi Penyakit Jantung Koroner”, Seminar Nasional Aplikasi Teknologi Informasi (SNATI), Yogyakarta, Juni 16, 2007.
[4]A. R. G. Silva, H. M. Oliveira, and R. D. Lins, “Converting ECG and Other Paper Legated Biomedical Maps into Digital Signals”, Springer-Verlag Berlin Heidelberg, pp. 21–28, 2008.
[5]M. B. Tayel and M. E. El-Bouridy, “ECG Images Classification using Artificial Neural Network Based on Several Feature Extraction Methods”, International Conference on Computer Engineering & Systems (ICCES), 978-1-4244-2116-9/08, IEEE, 2008.
[6]M. Fauziyah, T. Sriwidodo, and Litasari, “Pengembangan Jaringan Syaraf Tiruan Backpropagation Untuk Klasifikasi Isyarat EKG”, Prosiding SENTIA, Politeknik Negeri Malang, 2009.
[7]P. Swamy, S. Jayaraman, and M. G. Chandra, “An Improved Method for Digital Time Series Signal Generation From Scanned ECG Records”, International Conference on Bioinformatics and Biomedical Technology. 2010.
[8]D. Thanapatay, C. Suwansaroj, and C. Thanawattano, “ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines”, International Conference on Electronics and Information Engineering (ICEIE ), Vol.1, 978-1-4244-7680-0, IEEE, 2010.
[9]V. Damodaran, S. Jayaraman, and S. Poonguzhali, “A Novel Method to Extract ECG Morphology From Scanned ECG Records”,http://ieeexplore.ieee.org.ezproxy.ugm.ac.id/ielx5/6018838/6026799/06026803.pdf?tp=&arnumber=6026803&isnumber=6026799,2011.
[10]V. Kumar, J. Sharma, S. Ayub, and J. P. Saini, “Extracting Samples As Text From ECG Strips For ECG Analysis Purpose”, Fourth International Conference on Computational Intelligence and Communication Networks, IEEE, 2012, DOI 10.1109/CICN.2012.110.
[11]R. Lehtinen, H. Holst, L. Edenbrandt, O. Pahlm, and J. Malmivuo, “Artificial Neural Network for the Exercise Electrocardiographic Detection of Coronary Artery Disease”, 2nd International Conference on Bioelectromagnetism, , Melbourne, Australia, February 1998.
[12]R. Silipo and C. Marchesi, “Artificial Neural Networks for Automatic ECG Analysis”, IEEE Transactions On Signal Processing, Vol.46, No.5, May1998.
[13]C. Hasani, “Analisis Elektrokrdiogram Menggunakan Jaringan Syaraf Tiruan Untuk Mendeteksi Kondisi Jantung Pasien”, Tesis, Program Studi Teknik Elektro, UGM, Yogyakarta, 2002.
[14]E. R. Adams and A. Choi, “Using Neural Networks to Predict Cardiac Arrhythmias”, IEEE International Conference on Systems, Man, and Cybermetics, October 14-17, COEX, Seoul, Korea, 2012.
[15]S. Dandotiya and M. Ramaiya, “Review of ECG Signal De-noising and Peaks Detection Techniques”, International Journal of Advanced Engineering Research and Science (IJAERS), ISSN: 2349-6495, Vol-3, Issue-3, March 2016.
[16]M. Aqil, A. Jbari, and A. Bourouhou, “Adaptive ECG Wavelet analysis for R-peaks Detection”, 2nd International Conference on Electrical and Information Technologies (ICEIT), IEEE, 978-1-4673-8469-8/16, 2016.
[17]A. A. Ahmad, A. I. Kuta, and A. Z. Loko, "Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.9, No.2, pp.19-26, 2017. DOI: 10.5815/ijigsp.2017.02.03.
[18]B. Destyningtias, S. Heranurweni, and T. Nurhayati, “Segmentasi Citra Dengan Metode Pengambangan”, eLEKTRIKA, Vol.2, No.1, pp: 39 – 49, ISSN: 2085-0565, 2010.
[19]R. C. Gonzales and R. E. Wood, Digital Image Processing, Second Edition, Pearson Prentice Hall, 2002.
[20]S. Samarsinghe, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition, Auerbach Publications: Taylor & Francis Group, 2007.
[21]J. Pan and W. J. Tompkins, “A Real-Time QRS Detection Algorithm”, IEEE Transactions On Biomedical Engineering, Vol. BME-32, No. 3, March 1985.
[22]R. A. Balda, G. Diller, E. Deardorff, J. Doue, and P. Hsieh, “The HP ECG Analysis Program”, Trends in Computer-Processed Electrocardiograms. J. H. Van Bemnel and J. L. Willems, (eds.) Amsterdam, The Netherlands: North Holland, pp. 197–205, 1977.
[23]M. L. Ahlstrom and W. J. Tompkins, “Automated high-speed analysis of Holter tapes with microcomputers”, IEEE Trans. Biomed. Eng., BME-30: 651–57, 1983.
[24]G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, “A Comparison Of The Noise Sensitivity Of Nine QRS Detection Algorithms”, IEEE Trans. Biomed. Eng., BME-37: 85–97, 1990.
[25]W. J. Tompkins, Biomedical Digital Signal Pro-cessing, Prentice-Hall Inc., Englewood Cliffs, N.J., Eth Bib: +521 458, 1993.
[26]Dataq, A Closer Look At The Advanced CODAS Moving Average Algorithm, 2016,http://www.dataq.com/resources/pdfs/article_pdfs/an14.pdfl
[27]J. Z. Tsai, Biomedical Signal Processing, ElectroBioMedical Laboratory, Departement of Electrical Engineering, National Central University, Taiwan, 2011, http://www.ee.ncu.edu.tw/~jztsai/EE8018/lectureNote/,
[28]J. Goette, Event Detection: QRS-Complexes in ECG Signals, BioMedSigProcAna, Bern University of Applied Sciences, 2016.
[29]A. M. S. Hendriawan, “Implementasi Metode Pengujian Sistem Pendeteksi Kelainan Ritme Jantung Berbasis Mikrokontroler 8-Bit”, Tesis, Program Pascasarjana Fakultas Teknik, Universitas Gadjah Mada Yogyakarta, 2013.
[30]R. Acharya, J. S. Suri, and J. A. E. Spaan, “Advances in Cardiac Signal Processing”, SPRINGER Verlag, 2007.
[31]J. Parák, and J. Havlík, “ECG Signal Processing and Heart Rate Frequency Detection Methods”, In Proceedings of Technical Computing Prague, 2011.
[32]M. S. Thaler, Satu-satunya Buku EKG Yang Anda Perlukan, Buku Kedokteran ECG, 2012.
[33]M. J. Vidya and D. Kavya, “Analysis Of ECG Signal Using Matlab For The Detection Of Ischemia”, International Journal Of Inovative Research & Development, Vol.2, Issue:4, April 2013.
[34]D. N. K. Hardani, “Analisis Kondisi Emosi Melalui Isyarat Elektrokardiogram”, Tesis, Program Pascasarjana, Fakultas Teknik, UGM Yogyakarta, 2014.