Nada Fitrieyatul Hikmah, Achmad Arifin, Tri Arief Sardjono


ECG signals have very features time-varying morphology, distinguished as P wave, QRS complex, and T wave. Delineation in ECG signal processing is an important step used to identify critical points that mark the interval and amplitude locations in the features of each wave morphology. The results of ECG signal delineation can be used by clinicians to associate the pattern of delineation point results with morphological classes, besides delineation also produces temporal parameter values of ECG signals. The delineation process includes detecting the onset and offset of QRS complex, P and T waves that represented as pulse width, and also the detection of the peak from each wave feature. The previous study had applied bandpass filters to reduce amplitude of P and T waves, then the signal was passed through non-linear transformations such as derivatives or square to enhance QRS complex. However, the spectrum bandwidth of QRS complex from different patients or same patient may be different, so the previous method was less effective for the morphological variations in ECG signals. This study developed delineation from the ECG feature extraction based on multiresolution analysis with discrete wavelet transform. The mother wavelet used was a quadratic spline function with compact support. Finally, determination of R, T, and P wave peaks were shown by zero crossing of the wavelet transform signals, while the onset and offset were generated from modulus maxima and modulus minima. Results show the proposed method was able to detect QRS complex with sensitivity of 97.05% and precision of 95.92%, T wave detection with sensitivity of 99.79% and precision of 96.46%, P wave detection with sensitivity of 56.69% and precision of 57.78%. The implementation in real time analysis of time-varying ECG morphology will be addressed in the future research.

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J. L.Garvey, "ECG Techniques and Technologies," Emergency Medicine Clinics of North America, vol. 24, pp. 209-255, 2006.

N. V. Thakor, J. G. Webster, and W.J. Tompkins, "Estimation of QRS Complex Power Spectra for Design of a QRS Filter," IEEE Transactions on Biomedical Engineering, vol. BME-31, no. 11, pp. 702-706, 1984.

G. S. Furno and W. J. Tompkins, "QRS Detection Using Automata Theory in A Battery-powered Microprocessor System," IEEE Frontiers of Engineering in Health Care, vol. 4, pp. 155-158, 1982.

S. E. Dobbs, N. M. Schmitt, and H. S. Ozemek, "QRS Detection by Template Matching Using Real-time Correlation on A Microcomputer," Journal of Clinical Engineering, vol. 9, pp. 197-212, 1984.

J. Pan and W. J. Tompkins, "A Real-time QRS Detection Algorithm," IEEE Transactions on Biomedical Engineering, vol. BME-32, pp. 230-236, 1985.

N. F. Hikmah, A. Arifin, T. A. Sardjono, and E. A. Suprayitno, "A Signal Processing Framework for Multimodal Cardiac Analysis," in Proc. International Seminar on Intelligent Technology and Its Applications, pp. 125-130, 2015.

C. Li, C. Zheng, and C. Tai, "Detection of ECG Characteristic Points Using Wavelet Transforms," IEEE Transactions on Biomedical Engineering, vol. 42, no. 1, 1995.

H. A. N. Dinh, D. K. Kumar, N. D. Pah, and P. Burton, "Wavelets for QRS Detection," Australas. Phys.Eng. Sci. Med., vol. 24, no 4, pp. 207-211, 2001.

Y. Wang, X. Chen, W. Zhu, and L. Wang, "P Wave Detection and Delineation Based on Local Distance Transform," in Proc. IEEE TrustCom-BigDataSE-ISPA, vol.8, 2016.

N. F. Hikmah, A. Arifin, T. A. Sardjono, and E. A. Suprayitno, "A Sequential Hypothesis Testing of Multimodal Cardiac Analysis," in Proc. Asea Uninet Scientific and Plenary Meeting, pp. 63-77, 2016.

M. Bahoura, M. Hassani, and M. Hubin, "DSP Implementation of Wavelet Transform for Real Time ECG Wave Forms Detection and Heart Rate Analysis," Computer Methods and Programs in Biomedicine, vol.52, pp. 35-44, 1997.

J. P. Martinez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, "A Wavelet-Based ECG Delineator: Evaluation on Standard Databases," IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, 2004.

W. Srisawat, "Implementation of Real Time Feature Extraction of ECG Using Discrete Wavelet Transform," in Proc. International Conference on Electrical Engineering/Electronics, Computer, Telecommunication and Information Technology, 2013.

H. Hajimolahoseini, J. Hashemi, and D. P. Redfearn, "ECG Delineation for QT Interval Analysis Using an Unsupervised Learning Method," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2541-2545, 2018.

J. L. Ramirez, J. A. Torres, and O. Camacho, "Noise Reduction and Delineation of ECG Signal," in Proc. IEEE International Conference on Information Systems and Computer Science, pp. 9-15, 2018.

A. Rao, P. Gupta, and P. K. Ghosh, "P- and T-wave Delineation in ECG Signals Using Parametric Mixture Gaussian and Dynamic Programming," Biomedical Signal Processing and Control, vol. 51, pp. 328-337, 2019.

A. Theolis, Computational Signal Processing with Wavelets, Switzerland: Springer International Publishing, 2017.

S. Mallat, "A Theory for Multiresolution Signal Decomposition: The Wavelet Representation," IEEE Transactions Pattern Anal. Machine Intel, vol. 11, pp. 674-693, 1989.

W.J. Tompkins, Biomedical Digital Signal Processing, Prentice Hall, New Jersey, 2000.



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