Perfoma Discrete Wavelet Transform dalam Denoising Sinyal EKG Berdasarkan Evaluasi Signal-to-Noise Rasio
Abstract
Keywords
Full Text:
PDFReferences
M. Aqil, A. Jbari, and A. Bourouhou, “ECG Signal Denoising by Discrete Wavelet Transform.,” Int. J. Online Eng., vol. 13, no. 9, 2017.
G. T. Ramadhani, A. Adiwijaya, and D. Q. Utama, “Klasifikasi Penyakit Aritmia Melalui Sinyal Elektrokardiogram (ekg) Menggunakan Metode Local Features Dan Support Vector Machine,” eProceedings Eng., vol. 5, no. 1, 2018.
M. Risnasari, “Penekanan Noise Pada Sinyal EKG Menggunakan Transformasi Wavelet,” J. Ilm. Edutic Pendidik. dan Inform., vol. 1, no. 1, 2014.
S. Kaplan Berkaya, A. K. Uysal, E. Sora Gunal, S. Ergin, S. Gunal, and M. B. Gulmezoglu, “A survey on ECG analysis,” Biomed. Signal Process. Control, vol. 43, pp. 216–235, 2018.
O. Heriana and A. M. Al Misbah, “Comparison of wavelet family performances in ECG signal denoising,” J. Elektron. dan Telekomun., vol. 17, no. 1, pp. 1–6, 2017.
S. L. Joshi, R. A. Vatti, and R. V Tornekar, “A survey on ECG signal denoising techniques,” in 2013 International Conference on Communication Systems and Network Technologies, 2013, pp. 60–64.
I. Mohapatra, P. Pattnaik, and M. N. Mohanty, Cardiac Failure Detection Using Neural Network Model with Dual-Tree Complex Wavelet Transform, vol. 846. Springer Singapore, 2019.
N. A. Polytechnic, “Detection of Shockable Ventricular Arrhythmia using Optimal Orthogonal Wavelet Filters Detection of Shockable Ventricular Arrhythmia using Optimal Orthogonal Wavelet Filters,” no. January, 2019.
J. D. Roberts et al., “Electrocardiographic intervals associated with incident atrial fibrillation: Dissecting the QT interval,” Hear. Rhythm, vol. 14, no. 5, pp. 654–660, 2017.
S. B. Anuja, U. N. K, and S. T. Sukanya, “ECG Signals Classification using Statistical and Wavelet Features,” Int. J. Recent Technol. Eng., vol. 8, no. 5, pp. 1497–1504, 2020.
D. Zhang et al., “An ECG signal de-noising approach based on wavelet energy and sub-band smoothing filter,” Appl. Sci., vol. 9, no. 22, 2019.
S. Mandala, Y. N. Fuadah, M. Arzaki, and F. E. Pambudi, “Performance analysis of wavelet-based denoising techniques for ECG signal,” in 2017 5th International Conference on Information and Communication Technology (ICoIC7), 2017, pp. 1–6.
W. Jenkal, R. Latif, A. Toumanari, A. Dliou, O. El B’charri, and F. M. R. Maoulainine, “An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform,” Biocybern. Biomed. Eng., vol. 36, no. 3, pp. 499–508, 2016.
G. Kaushik, H. P. Sinha, and L. Dewan, “BIOMEDICAL SIGNALS ANALYSIS BY DWT SIGNAL DENOISING WITH NEURAL NETWORKS.,” J. Theor. Appl. Inf. Technol., vol. 62, no. 1, 2014.
H. Serhal, N. Abdallah, J.-M. Marion, P. Chauvet, M. Oueidat, and A. Humeau-Heurtier, “Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG,” Comput. Biol. Med., p. 105168, 2022.
“MIT-BIH Arrhythmia Database Directory (Introduction).” [Online]. Available: https://archive.physionet.org/physiobank/database/html/mitdbdir/intro.htm#annotations. [Accessed: 27-Jun-2022].
“MIT-BIH Arrhythmia Database v1.0.0.” [Online]. Available: https://physionet.org/content/mitdb/1.0.0/. [Accessed: 27-Jun-2022].
A. B. H. Adamou-Mitiche, L. Mitiche, and H. Naimi, “Three levels discrete wavelet transform elliptic estimation for ECG denoising,” in 2016 4th International Conference on Control Engineering & Information Technology (CEIT), 2016, pp. 1–5.
R. Von Borries, P. JH, and H. Nazeran, “Redundant Discrete Wavelet Transform for ECG Signal Processing (< Special Issue> Biosensors: Data Acquisition, Processing and Control),” Int. J. Biomed. Soft Comput. Hum. Sci. Off. J. Biomed. Fuzzy Syst. Assoc., vol. 14, no. 2, pp. 71–81, 2009.
S. Mallat, A wavelet tour of signal processing. Elsevier, 1999.
R. Von Borries, J. H. Pierluissi, and H. Nazeran, “Redundant discrete wavelet transform for ECG signal processing,” Biomed Soft Comput Hum Sci, vol. 14, no. 2, pp. 69–80, 2009.
H.-Y. Lin, S.-Y. Liang, Y.-L. Ho, Y.-H. Lin, and H.-P. Ma, “Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals,” Irbm, vol. 35, no. 6, pp. 351–361, 2014.
R. S. Singh, B. Singh, S. Ramesh, and K. Sunkaria, “Arrhythmia detection based on time – frequency features of heart rate variability and back-propagation neural network,” Iran J. Comput. Sci., vol. 2, no. 4, pp. 245–257, 2019.
M. Systems, S. Sabut, M. Mohanty, and P. K. Biswal, “Machine learning approach to recognize ventricular arrhythmias using VMD based features,” no. April, 2019.
Y. A. Altay and A. S. Kremlev, “Signal-to-Noise Ratio and Mean Square Error Improving Algorithms Based on Newton Filters for Measurement ECG Data Processing,” in 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2021, pp. 1590–1595.
C. Sawant and H. T. Patii, “Wavelet based ECG signal de-noising,” in 2014 First International Conference on Networks & Soft Computing (ICNSC2014), 2014, pp. 20–24.
DOI: https://doi.org/10.33633/tc.v21i4.6961
Article Metrics
Abstract view : 165 timesPDF - 170 times
Refbacks
- There are currently no refbacks.
Diterbitkan Oleh :
Jurnal Techno.Com terindex di :
Jurnal Teknologi Informasi Techno.Com (p-ISSN : 1412-2693, e-ISSN : 2356-2579) diterbitkan oleh LPPM Universitas Dian Nuswantoro Semarang. Jurnal ini di bawah lisensi Creative Commons Attribution 4.0 International License.