Penerapan Filter Digital untuk Menghilangkan Gangguan pada Sinyal Elektrokardiogram

Santoso Santoso, Ratna Hartayu, Ahmad Ridho’i, Balok Hariadi, Kukuh Setydjit, Lutfi Agung Swarga, M Ary Heryanto

Abstract

This research examines the application of Finite Impulse Response (FIR) filters in processing ECG signals to eliminate noise and enhance signal quality. Using ECG recordings from the MIT-BIH database, the original signal contaminated by noise was processed with FIR filters, and the results were compared with signals filtered using the Infinite Impulse Response (IIR) method. The analysis results indicate that FIR filters are effective in reducing noise and improving the accuracy of morphological analysis, with a post-filtering Signal-to-Noise Ratio (SNR) of 1.24 dB. Although the SNR improvement is still relatively low, this study highlights the importance of applying appropriate filtering techniques to support more accurate medical diagnoses. Future research is recommended to explore the performance comparison of FIR filters with other signal processing techniques, as well as efforts to further enhance signal quality and SNR.

Keyword: ECG Signal, FIR Filter, Signal Processing, Signal-to-Noise Ratio (SNR), Morphological Analysis

Keywords

Biomedic

Full Text:

Untitled PDF

References

A. E. Curtin, K. V. Burns, A. J. Bank, and T. I. Netoff, “QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients,” IEEE J. Transl. Eng. Health Med., vol. 6, pp. 1–11, 2018, doi: 10.1109/JTEHM.2018.2844195.

T. Thamaraimanalan and P. Sampath, “A low power fuzzy logic based variable resolution ADC for wireless ECG monitoring systems,” Cogn. Syst. Res., vol. 57, pp. 236–245, Oct. 2019, doi: 10.1016/j.cogsys.2018.10.033.

C. B. Güngör, P. P. Mercier, and H. Töreyin, “A 2.2 nW Analog Electrocardiogram Processor Based on Stochastic Resonance Achieving a 99.94% QRS Complex Detection Sensitivity,” IEEE Trans. Biomed. Circuits Syst., vol. 17, no. 1, pp. 33–44, Feb. 2023, doi: 10.1109/TBCAS.2023.3235786.

H.-T. Chiang, Y.-Y. Hsieh, S.-W. Fu, K.-H. Hung, Y. Tsao, and S.-Y. Chien, “Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders,” IEEE Access, vol. 7, pp. 60806–60813, 2019, doi: 10.1109/ACCESS.2019.2912036.

R. V. Savitha, S. R. Breesha, and X. F. Joseph, “Pre processing the abdominal ECG signal using combination of FIR filter and principal component analysis,” in 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], Mar. 2015, pp. 1–4. doi: 10.1109/ICCPCT.2015.7159460.

N. Singh, S. Ayub, and J. P. Saini, “Design of Digital IIR Filter for Noise Reduction in ECG Signal,” in 2013 5th International Conference and Computational Intelligence and Communication Networks, Sep. 2013, pp. 171–176. doi: 10.1109/CICN.2013.45.

S. Saxena, R. Jais, and M. K. Hota, “Removal of Powerline Interference from ECG Signal using FIR, IIR, DWT and NLMS Adaptive Filter,” in 2019 International Conference on Communication and Signal Processing (ICCSP), Apr. 2019, pp. 0012–0016. doi: 10.1109/ICCSP.2019.8698112.

S. Selvaraj, P. Ramya, R. Priya, and C. Ramya, “Filtering the ECG Signal towards Heart Attack Detection using Motion Artifact Removal Technique,” in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Feb. 2021, pp. 185–188. doi: 10.1109/ICICV50876.2021.9388515.

T. Yadav and R. Mehra, “Denoising and SNR improvement of ECG signals using wavelet based techniques,” in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Oct. 2016, pp. 678–682. doi: 10.1109/NGCT.2016.7877498.

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.

S. Nayak, M. Nayak, S. Matri, and K. P. Sharma, “Synthesis and Analysis of Digital IIR Filters for Denoising ECG Signal on FPGA,” in Evolving Networking Technologies: Developments and Future Directions, Wiley, 2023, pp. 189–210. doi: 10.1002/9781119836667.ch12.

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.

M. Saeed, D. John, and B. Cardiff, “Accurate Reconstruction of ECG Signals using Chebyshev Polynomials,” in 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Oct. 2022, pp. 1–2. doi: 10.1109/ICECS202256217.2022.9970820.

Y. A. Altay and A. S. Kremlev, “Comparative analysis of ECG signal processing methods in the time-frequency domain,” in 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Jan. 2018, pp. 1058–1062. doi: 10.1109/EIConRus.2018.8317272.

Chr. Zywietz, B. Widiger, and R. Fischer, “A system for comprehensive comparison of serial ECG beats and serial ECG recordings,” in Computers in Cardiology, 2003, Sep. 2003, pp. 689–692. doi: 10.1109/CIC.2003.1291249.

G. B. Moody and R. G. Mark, “MIT-BIH Arrhythmia Database.” physionet.org, 1992. doi: 10.13026/C2F305.

DOI: https://doi.org/10.1593/jreec.v4i2.6711