ECG Waveform Classification Based on P-QRS-T Wave Recognition
DOI:
https://doi.org/10.21928/uhdjst.v2n2y2018.pp7-14Keywords:
Electrocardiogram; QRS Wave; Feature Extraction; ECG SignalAbstract
Electrocardiogram (ECG) is a periodic signal reflects the activity of the heart. ECG waveform is an important issue to define the heart function so it is helpful to recognize the type of heart diseases. ECG graph generate a lot of information that is converted into electrical signal with standard values of amplitude and duration. The main problem raised in this measurement is the mixing between normal and abnormal, in addition some time there are overlapping between the P-QRS-T waveform. This research aims to offer an efficient approach to measure all parts of P-QRS-T waveform in order to give a correct decision of heart functionality. The implemented approach including any steps; preprocessing, baseline process, feature extraction and diagnosis. The obtained result indicated an adequate recognition rate to verify the heart functionality.
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[3] S.K. Berkaya, A.K. Uysal, E.S. Gunal, S. Ergin and M.B. Gulmezoglu. A survey on ECG analysis. Biomedical Signal Processing and Control, vol. 43, pp. 216-235, May. 2018.
[4] H. Sharma and K.K. Sharma. ECG-derived respiration using Hermite expansion. Biomedical Signal Processing and Control, vol. 39, pp. 312-326, Jan. 2018.
[5] R.L. Lux. Basis and ECG measurement of global ventricular repolarization. Journal of Electrocardiology, vol. 50, no. 6, pp. 792- 797, Dec. 2017.
[6] L. Mesin. Heartbeat monitoring from adaptively down-sampled electrocardiogram. Computers in Biology and Medicine, vol. 84, 217-225, May. 2017.
[7] D. Poulikakos and M. Malik. Challenges of ECG monitoring and ECG interpretation in dialysis units. Journal of Electrocardiology, vol. 49, no. 6, pp. 855-859, Dec. 2016.
[8] G. Zhang, T. Wu, Z. Wan, Z. Song and F. Chen. A new method to detect ventricular fibrillation from CPR artifact-corrupted ECG based on the ECG alone. Biomedical Signal Processing and Control, vol. 29, pp. 67-75, Aug. 2016.
[9] P.W. Macfarlane, S.M. Lloyd, D. Singh, S. Hamde and V. Kumar. Normal limits of the electrocardiogram in Indians. Journal of Electrocardiology, vol. 48, no. 4, pp. 652-668, Aug. 2015.
[10] F. Gargiulo, A. Fratini, M. Sansone and C. Sansone. Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction. Computer Methods and Programs in Biomedicine, vol. 121, no. 3, pp. 127-136, Oct. 2015.
[11] M. R. Homaeinezhad, M.E. Moshiri-Nejad and H. Naseri. A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles. Computers in Biology and Medicine, vol. 44, pp. 66-75, Jan. 2014.
[12] R.J. Martis, U.R. Acharya and H. Adeli. Current methods in electrocardiogram characterization. Computers in Biology and Medicine, vol. 48, pp. 133-149, May. 2014.
[13] M.S. Al-Ani and A.A. Rawi. “ECG beat diagnosis approach for ECG printout based on expert system”. International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 4, Apr. 2013.
[14] K.N.V.P.S. Rajesh and R. Dhuli. Classification of imbalanced ECG beats using re-sampling techniques and Ada boost ensemble classifier. Biomedical Signal Processing and Control, vol. 41, pp. 242-254, Mar. 2018.
[15] M.S. Al-Ani and K.M.A. Alheeti. “Precision statistical analysis of images based on brightness distribution”. Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 4, 99- 104, 2017.
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[24] R.E. Gregg, S.H. Zhou and A.M. Dubin. Automated detection of ventriculr pre-excitation in pediatric 12-lead ECG. Journal of Electrocardiology, vol. 49, no. 1, pp. 37-41, Jan. 2016.
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[35] M. AlMahamdy and H.B. Riley. Performance study of differentdenoising methods for ECG signals. Procedia Computer Science, vol. 37, pp. 325-332, 2014.
[36] M.K. Yapici, T. Alkhidir, Y.A. Samad and K. Liao. Graphene-clad textile electrodes for electrocardiogram monitoring. Sensors and Actuators B: Chemical, vol. 221, pp. 1469-1474, Dec. 2015.
[37] Z. Wang, F. Wan, C.M. Wong and L. Zhang. Adaptive Fourier decomposition based ECG Denoising. Computers in Biology and Medicine, vol. 77, pp. 195-205, Oct. 2016.
[38] C. Zou, Y. Qin, C. Sun, W. Li and W. Chen. Motion artifact removal based on periodical property for ECG monitoring with wearable systems. Pervasive and Mobile Computing, vol. 40, pp. 267-278, Sep. 2017.
[39] Q. Yu, H. Yan, L. Song, W. Guo and Y. Zhao. Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction. Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 448- 455, 2018.
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Published
2018-07-25
How to Cite
Al-Ani, M. S. (2018). ECG Waveform Classification Based on P-QRS-T Wave Recognition. UHD Journal of Science and Technology, 2(2), 7–14. https://doi.org/10.21928/uhdjst.v2n2y2018.pp7-14
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