Pengembangan Filter Adaptif Berbasis Least Mean Square untuk Pengurangan Noise pada Sinyal Elektrokardiogram

Kukuh Setyadjit, Santoso Santoso, Muhammad Amsyaril Hariz, Reyhan Ihsan Muhammad, Puji Slamet, Aris Heri Andriawan, Ratna Hartayu

Abstract


Electrocardiogram (ECG) signals are often affected by various types of noise, such as electromagnetic interference, patient body movement, and disturbances from other medical devices, which can degrade the signal quality and hinder accurate detection of heart disorders. This study aims to compare the performance of various filters in reducing noise in ECG signals, focusing on the Highpass-Lowpass filter and the Least Mean Square (LMS) adaptive filter. The testing was conducted using metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), average amplitude, total error, and computation time. The experimental results show that the LMS filter provides the best results, with an MSE value of 0.0045, SNR of 21.5 dB, and total error of 4.78, indicating its ability to produce a cleaner signal compared to the Highpass-Lowpass filter. The LMS filter also demonstrates good computational efficiency, with a time of 0.102 seconds. With its ability to dynamically adjust filter parameters, the LMS filter proves effective in reducing both low and high-frequency noise in ECG signals. This study shows that the LMS filter can be effectively applied to process ECG signals contaminated by noise and contributes to improving the accuracy of heart disorder diagnosis

Keywords


Electrocardiogram, Noise Reduction, Least Mean Square Filter, Signal-to-Noise Ratio, Filter Performance

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References


A. Burguera, “Fast QRS Detection and ECG Compression Based on Signal Structural Analysis,” IEEE J. Biomed. Health Inform., vol. 23, no. 1, pp. 123–131, Jan. 2019, doi: 10.1109/JBHI.2018.2792404.

F. A. Elhaj, N. Salim, A. R. Harris, T. T. Swee, and T. Ahmed, “Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals,” Comput. Methods Programs Biomed., vol. 127, pp. 52–63, Apr. 2016, doi: 10.1016/j.cmpb.2015.12.024.

J. D. Ćertić and L. D. Milić, “Investigation of computationally efficient complementary IIR filter pairs with tunable crossover frequency,” AEU - Int. J. Electron. Commun., vol. 65, no. 5, pp. 419–428, May 2011, doi: 10.1016/j.aeue.2010.05.004.

N. Das and M. Chakraborty, “Performance analysis of FIR and IIR filters for ECG signal denoising based on SNR,” in 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Nov. 2017, pp. 90–97. doi: 10.1109/ICRCICN.2017.8234487.

D. Jingwei and J. Wenwen, “Design of Digital Filter on ECG Signal Processing,” in 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Sep. 2015, pp. 1272–1275. doi: 10.1109/IMCCC.2015.273.

S. Santoso et al., “Penerapan Filter Digital untuk Menghilangkan Gangguan pada Sinyal Elektrokardiogram,” JREEC J. Renew. Energy Electron. Control, vol. 4, no. 2, pp. 36–42, 2024.

F. T. Kurniati and V. R. A. Febriyanto, “Pemodelan Filter Adaptif Untuk Perbaikan Kualitas Sinyal Audio Multi Wicara,” J. Sist. Dan Inform. JSI, vol. 9, no. 1, Art. no. 1, 2014.

S. Budiyanto and C. A. Pandawa, “PERBANDINGAN NLMS DAN RLS PADA ADAPTIVE NOISE CANCELLER MENGGUNAKAN LABVIEW,” vol. 18, no. 2.

C. Venkatesan, P. Karthigaikumar, and R. Varatharajan, “A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection,” Multimed. Tools Appl., vol. 77, no. 8, pp. 10365–10374, Apr. 2018, doi: 10.1007/s11042-018-5762-6.

M. Z. U. Rahman, R. A. Shaik, and D. V. R. K. Reddy, “Adaptive noise removal in the ECG using the Block LMS algorithm,” in 2009 2nd International Conference on Adaptive Science & Technology (ICAST), Jan. 2009, pp. 380–383. doi: 10.1109/ICASTECH.2009.5409698.




DOI: https://doi.org/10.31284/p.snestik.2025.7539

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