Performance Analysis of PPG Signal Denoising Method Using DWT and EMD for Detection of PVC and AF Arrhytmias
Analisis Performansi Metode Denoising Sinyal PPG Menggunakan DWT dan EMD untuk deteksi Aritmia PVC dan AF
DOI:
https://doi.org/10.34818/INDOJC.2022.7.2.648Keywords:
PPG, Denoising, PVC, AF, DWT, EMDAbstract
In the cardiac arrhythmia detection system using a Photoplethysmography (PPG) sensor, noise is often found in the PPG signal due to internal and external factors in the signal retrieval process. So it is necessary to do a denoising process to remove noise before the signal is used. This study aims to test the Discrete wavelet transform (DWT) and Empirical Mode Decomposition (EMD) methods in removing noise from the PPG signal and to test the denoising signal on the Premature Arrhythmia Verticular Contractions (PVC) and Atrial Fibrillation (AF) detection systems. The parameters used to compare the performance of the denoising method are Mean Square Error (MSE), Signal to Noise Ratio (SNR), Accuracy, F1, Precision, and Recall. The method with the highest SNR, Accuracy, F1, Precision, and Recall values ​​and the lowest MSE values ​​is the best denoising method.
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References
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[2] P. Madona, “Akuisisi Data Sinyal Photoplethysmograph (PPG) Menggunakan Photodioda,†Jurnal Elektro dan Mesin Terapan, vol. 2, no. 2, 2016, doi: 10.35143/elementer.v2i2.187.
[3] A. Caizzone, A. Boukhayma, and C. Enz, “Comprehensive noise analysis in PPG read-out chains,†2017. doi: 10.1109/ICNF.2017.7985971.
[4] N. Paradkar and S. R. Chowdhury, “Cardiac arrhythmia detection using photoplethysmography,†2017. doi: 10.1109/EMBC.2017.8036775.
[5] M. AlMahamdy and H. B. Riley, “Performance study of different denoising methods for ECG signals,†in Procedia Computer Science, 2014, vol. 37. doi: 10.1016/j.procs.2014.08.048.
[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,†Biocybernetics and Biomedical Engineering, vol. 36, no. 3, 2016, doi: 10.1016/j.bbe.2016.04.001.
[7] M. Rakshit and S. Das, “An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter,†Biomedical Signal Processing and Control, vol. 40, pp. 140–148, Feb. 2018, doi: 10.1016/J.BSPC.2017.09.020.
[8] S. Kumar, D. Panigrahy, and P. K. Sahu, “Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique,†Biocybernetics and Biomedical Engineering, vol. 38, no. 2, 2018, doi: 10.1016/j.bbe.2018.01.005.
[9] S. Saxena, R. Jais, and M. K. Hota, “Removal of powerline interference from ECG signal using FIR, IIR, DWT and NLMS adaptive filter,†2019. doi: 10.1109/ICCSP.2019.8698112.
[10] Z. Wang, J. Zhu, T. Yan, and L. Yang, “A new modified wavelet-based ECG denoising,†Computer Assisted Surgery, vol. 24, no. sup1, 2019, doi: 10.1080/24699322.2018.1560088.
[11] R. Devi, H. K. Tyagi, and Di. Kumar, “Performance Comparison and Applications of Sparsity Based Techniques for Denoising of ECG Signal,†2019. doi: 10.1109/SPIN.2019.8711632.
[12] K. S. S. Sujan, R. S. Pridhvi, K. P. Priya, and R. V. Ramana, “Performance analysis for the Feature Extraction algorithm of an ECG signal,†2015. doi: 10.1109/ICIIECS.2015.7193100.
[13] G. Joseph, A. Joseph, G. Titus, R. M. Thomas, and D. Jose, “Photoplethysmogram (PPG) signal analysis and wavelet de-noising,†2014. doi: 10.1109/AICERA.2014.6908199.
[14] M. Mohanty, P. Biswal, A. Subudhi, and S. Sabut, “Classification of Ventricular Arrhythmias Using DWT Based VMD Features,†2018. doi: 10.1109/ICDSBA.2018.00100.
[15] M. Rani, Ekta, and R. Devi, “Arrhythmia discrimination using support vector machine,†in 4th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2017, 2017, vol. 2017-January. doi: 10.1109/ISPCC.2017.8269690.
[16] R. Hayati and R. Kurnia, “Simulasi Unjuk Kerja Discrete Wavelet Transform (DWT) dan Discrete Cosine Transform (DCT) untuk Pengolahan Sinyal Radar di Daerah yang Ber-Noise Tinggi,†JURNAL NASIONAL TEKNIK ELEKTRO, vol. 3, no. 1, 2014, doi: 10.25077/jnte.v3n1.53.2014.
[17] X. Li, L. Dong, B. Li, Y. Lei, and N. Xu, “Microseismic signal denoising via empirical mode decomposition, compressed sensing, and soft-thresholding,†Applied Sciences (Switzerland), vol. 10, no. 6, 2020, doi: 10.3390/app10062191.
[18] C. U. Kumari et al., “Heart Rhythm Abnormality Detection and Classification using Machine Learning Technique,†2020. doi: 10.1109/ICOEI48184.2020.9142914.
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Published
2022-08-01
How to Cite
Aniq Wafa, M., Mandala, S., & Pramudyo , M. (2022). Performance Analysis of PPG Signal Denoising Method Using DWT and EMD for Detection of PVC and AF Arrhytmias: Analisis Performansi Metode Denoising Sinyal PPG Menggunakan DWT dan EMD untuk deteksi Aritmia PVC dan AF. Indonesian Journal on Computing (Indo-JC), 7(2), 35–44. https://doi.org/10.34818/INDOJC.2022.7.2.648
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Computer Science
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