Comparison of Fetal-Phonocardiogram Signal Denoising based on Real-Valued and Complex Wavelet Transform
Abstract
Monitoring of Fetal Heart Rate (FHR) in the pregnancy period commonly uses the Doppler-based instruments despite having several disadvantages, such as high-cost and complexity of the monitoring system. Implementation of the passive and non-invasive method based on fetal phonocardiogram (fPCG), the acoustic recording of fetus cardiac signal, can be used as a potentially economical long-term monitoring device for diagnosis. Because the interference signal from the maternal women exists, the matured denoising technique was needed to implement the fPCG method to diagnose the fetus' well-being condition. The denoising system based on Dual-tree Complex Wavelet Transforms (DTCWT) was proposed in this paper. The proposed method was evaluated using Signal to Noise Ratio (SNR). Based on the experiment result from 37 fPCG signals from physio.net, the DTCWT system performance was compared with the Discrete Wavelet Transform (DWT). There were 24 CWT’s denoised fPCG signals that have successfully outperformed DWT’s SNR. DTCWT has also reduced the noises in the range of 30 Hz–80 Hz. Also, it emphasized the existence of dominant frequencies in the range of 60 Hz–65 Hz.
References
S. Vaisman, S.Y. Salem, G. Holcberg, dan A.B. Geva, “Passive Fetal Monitoring by Adaptive Wavelet Denoising Method,” Computer in Biology and Medicine, Vol. 42, No. 2, hal. 171-179, 2012.
V.S. Chourasia dan Anil Kumar Tiwari, “Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals,” The Scientific World Journal, Vol. 2013, hal. 1-12, 2013.
E. Koutsiana, L.J. Hadjileontiadis, I. Chouvarda, dan A.H. Khandoker, “Detecting Fetal Heart Sounds by Means of Fractal Dimension Analysis in the Wavelet Domain,” 2017 39th Ann. Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2017, hal. 2201-2204.
P. Varady, L. Wildt, Z. Benyo, dan A. Hein., “An Advanced Method in Fetal Phoncardiography,” Computer Methods and Programs in Biomedicine, Vol. 71, No. 3, hal. 283-296, 2003.
K.K. Spyridou dan L.J. Hadjileontiadis, “Analysis of Fetal Heart Rate in Healthy and Pathological Pregnancies Using Wavelet-based Feature,” 29th Ann. Int. Conf. of the IEEE EMBS, 2007, hal. 1908-1911.
A. Sbrollini, A. Strazza, M. Caragiuli, C. Mozzoni, S. Tomassini, A. Agostinelli, M. Morettini, S. Fioretti, F. Di Nardo, L. Burattini, “Fetal Phonocardiogram, Denoising Wavelet Transformation: Robustness to Noise,” 2017 Computing in Cardiology, 2017, Vol. 44, hal. 1-4.
A. Strazza, A. Sbrollini, V. di Battista, R. Ricci, L. Trillini, I. Marcantoni, M. Morettini, S. Fioretti, dan L. Burattini, “PCG-Delineator: An Efficient Algorithm for Automatic Heart Sound Detection on Fetal Phonocardiography,” 2018 Computing in Cardiology Conference, 2018, Vol. 45, hal. 1-4.
D. Gradolewski dan G. Redlarski, “Wavelet-based Denoising Method for Real Phonocardiography Signal Recorded by Mobile Devices in Noisy Environment,” Computers in Biology and Medicine, Vol. 52, hal. 119-129, 2014.
F. Kovacs, C. Horvath, A. Balogh, dan G. Hosszu., “Extended Noninvasive Fetal Monitoring by Detailed Analysis of Data Measured with Phonocardiography,” IEEE Transcation on Biomedical Engineering, Vol. 58, No. 1, hal. 64-70, 2011.
R. Jaros, R. Martinek, R. Kahankova, J. Vanus, M. Fajkus, dan J. Nedoma, “Comparison of Fetal Phonocardiogram De-noising by Wavelet Transform and the FIR Filter,” 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), 2018, hal. 1-5.
A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.Ch. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C-K. Peng, dan H.E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, Vol. 101, No. 23, hal. e215-e220, 2000.
I. Fuadina, J. Hendry, dan D. Zulherman, “Performance Analysis of Fetalphonocardiogram Signal Denoising Using the Discrete Wavelet Transform,” Jurnal Infotel, Vol. 11, No. 4, 2019.
(2017) “Physionet wfdb Toolbox”, [Online], https://physionet.org/physiotools/matlab/wfdb-app-matlab, tanggal akses: 24-Okt-2019.
A.K. Mittra, N.K. Choudhary, dan A.S. Zadgaonkar, “Development of an Artificial Womb for Acoustical Simulation of Mother’s Abdomen,” Int. J. Biomedical Engineering and Technology, Vol. 1, No. 3, hal. 315-328, 2008.
M. Ruffo, M. Cesarelli, C. Jin, G. Gargiulo, A. McEwan, C. Sullivan, P. Bifulco, M. Romano, R.W. Shephard, dan A. van Schaik, “Non-Invasive Foetal Monitoring with a Combined ECG-PCG System,” dalam Biomedical Engineering, Trends in Electronics: Communications and Software, A.N. Laskovski, Ed. Rijeka, Croatia: InTech, 2011, hal. 347-366.
H.E. Bassil dan J.H. Dripps, “Real Time Processing and Analysis of Fetal Phonocardiographic Signals,” Clin. Phys. Physiol. Meas., Vol. 10, Suppl. B, hal. 67-74, 1989.
F. Kovacs, M. Torok, dan I. Habermajer, “A Rule-based Phonocardiographic Method for Long-term Fetal Heart Rate Monitoring,” IEEE Trans. on Biomed. Eng., Vol. 47, No 1, hal. 124-130, Jan. 2000.
F. Kovács, C. Horváth, Á.T. Balogh, dan G. Hosszú, “Fetal Phonocardiography--Past and Future Possibilities,” Computer Methods and Programs in Biomedicine, Vol. 104, No. 1, hal. 19-25, Okt. 2011.
D.L. Donoho dan I.M. Johnstone, “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of the American Statistical Association, Vol. 90, No. 432, hal. 1200-1224, 1995.
K.P. Soman, K.I. Ramachandran, dan N.G. Resmi, Insight into Wavelets: From Theory to Practice, 3rd ed., Delhi, India: PHI Learning, 2010.
K. Naveed, B. Shaukat, dan N. ur Rehman, “Dual Tree Complex Wavelet Transform-based Signal Denoising Method Exploiting Neighbourhood Dependencies and Goodness-of-fit Test,” Royal Society Open Science, Vol. 5, No. 9, hal. 1-18, Sep. 2018.
I.W. Selesnick, R.G. Baraniuk, dan N.G. Kingsbury, “The Dual-Tree Complex Wavelet Transform,” IEEE Signal Processing Magazine, Vol. 22, No. 6, hal. 123-151, Nov. 2005.
H. Choi, J. Romberg, R.G. Baraniuk, dan N. Kingsbury, “Hidden Markov Tree Modeling of Complex Wavelet Transforms,” Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), 2000, Vol. 1, hal. 133–136.
(2019) “Dual-Tree Wavelet Transform” [Online], https://fr.mathworks.com/help/wavelet/examples/dual-tree-wavelet-transforms.html, tanggal akses: 2-Nov-2019.
D. Sundararajan, Discrete Wavelet Transform, A Signal Processing Approach, Singapore, Singapore: Wiley & Sons Singapore Pte. Ltd., 2015.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
