Noise Reduction of Motion and EMG Artifacts in Holter ECG Using IIR Filters for Robust Arrhythmia Detection
DOI:
https://doi.org/10.37385/jaets.v7i2.9426Keywords:
Holter monitor, IIR Butterworth, ECG Lead, Motion Artifact, Muscle ArtifactAbstract
Ambulatory Holter electrocardiography (ECG) enables continuous monitoring for detecting transient arrhythmias; however, its diagnostic reliability is significantly degraded by motion artifacts and electromyographic (EMG) interference. Under severe motion artifact conditions, prior studies report that ambulatory ECG SNR can fall below −10 dB , although SNR levels vary substantially depending on activity type and electrode placement, reducing usable data segments and impairing arrhythmia detection. While advanced denoising methods such as wavelet transforms and deep learning achieve high accuracy, their computational complexity limits real-time deployment in resource-constrained embedded systems. This reveals a critical gap in lightweight methods that jointly optimize noise suppression, morphological preservation, and downstream diagnostic performance. This study proposes a computationally efficient IIR Butterworth bandpass filtering framework for real-time IoT-based Holter ECG systems. The system combines three-lead ECG acquisition, embedded processing on an ESP32, and real-time visualization. Performance is assessed using SNR, mean squared error (MSE), Pearson correlation, and confusion matrix-based detection metrics on ten male participants under controlled motion and muscle artifact conditions. Results demonstrate statistically significant SNR improvements for motion artifacts (ΔSNR = 9.47 ± 1.96 dB, t(9) = 15.28, p < 0.001) and EMG artifacts (ΔSNR = 16.73 ± 0.91 dB, t(9) = 58.11, p < 0.0001). Post-filtering morphological fidelity was high, with mean Pearson correlation of 0.963 for motion artifacts and 0.945 for muscle artifacts. These signal quality improvements translated into 95.3% post-filtering arrhythmia detection accuracy (sensitivity: ≈96.0%, specificity: ≥97.0%, F1-score: ≥95.0%), significantly exceeding the 70% minimum performance threshold adopted in this study as a conservative screening criterion (t(9) = 29.7, p < 0.001). Despite dataset limitations (n = 10), the proposed framework provides an effective trade-off between computational efficiency and diagnostic reliability, supporting scalable and real-time ambulatory ECG monitoring for early arrhythmia screening.
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References
Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological Measurement, 26(5), R155–R199. https://doi.org/10.1088/0967-3334/26/5/R01
Afaq Ahmad, Muhammad Lais, Dawar Awan, Muhammad Zia, Muhammad Uzair Khan, & Esha Farooq. (2025). ECG Denoising Using Wavelet Transform and Wiener Filter. Physical Education, Health and Social Sciences, 3(3), 97–105. https://doi.org/10.63163/jpehss.v3i3.685
Ali, M. A., Ali, S., & Khorsheed, A. (2023). ECG Signal Denoising Using Discrete Wavelet Transform. Journal of University of Duhok, 26(2), 450–463. https://doi.org/10.26682/sjuod.2023.26.2
Basu, S., & Mamud, S. (2020). Comparative Study on the Effect of Order and Cut off Frequency of Butterworth Low Pass Filter for Removal of Noise in ECG Signal. 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), 156–160. https://doi.org/10.1109/ICCE50343.2020.9290646
Bhavna Soni Pritaj Yadav. (2025). Review of Hybrid Deep Learning Techniques For Robust ECG Signal Classification by Addressing Noise and Class Imbalance. IITM Journal of Management and IT, 63–79. https://doi.org/10.65301/iitm.2025.17.2.926
Bing, P., Liu, W., Zhai, Z., Li, J., Guo, Z., Xiang, Y., He, B., & Zhu, L. (2024). A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Frontiers in Cardiovascular Medicine, 11. https://doi.org/10.3389/fcvm.2024.1277123
Chatterjee, S., Thakur, R. S., Yadav, R. N., Gupta, L., & Raghuvanshi, D. K. (2020). Review of noise removal techniques in ECG signals. IET Signal Processing, 14(9), 569–590. https://doi.org/10.1049/iet-spr.2020.0104
Clifford, G. D., Behar, J., Li, Q., & Rezek, I. (2012). Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiological Measurement, 33(9), 1419–1433. https://doi.org/10.1088/0967-3334/33/9/1419
Das, M., & Sahana, B. C. (2022). Deep-Learning-Based Approach for Automatic Detection and Correction of Ecg Artifacts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4308281
Das, M., & Sahana, B. C. (2025). Optimized Orthogonal Wavelet-Based Filtering Method for Electrocardiogram Signal Denoising. Journal of The Institution of Engineers (India): Series B, 106(3), 965–978. https://doi.org/10.1007/s40031-022-00796-6
Enayati, M., Farahani, N. Z., & Skubic, M. (2020). Machine Learning Approach for Motion Artifact Detection in Ballistocardiogram Signals. Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, 406–410. https://doi.org/10.1145/3421937.3421970
Galdos, J., Lopez Colque, N., Medina Rodirguez, A., Huarca Quispe, J., Rendulich, J., & Sulla Espinoza, E. (2024). Comparison and evaluation of LMS-derived algorithms applied on ECG signals contaminated with motion artifact during physical activities. Applied Computer Science, 20(1), 157–172. https://doi.org/10.35784/acs-2024-10
Ghaleb, F. A., Kamat, M. B., Salleh, M., Rohani, M. F., & Abd Razak, S. (2018). Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter. PLOS ONE, 13(11), e0207176. https://doi.org/10.1371/journal.pone.0207176
Hoffmann, J., Mahmood, S., Fogou, P. S., George, N., Raha, S., Safi, S., Schmailzl, K. J., Brandalero, M., & Hubner, M. (2020). A Survey on Machine Learning Approaches to ECG Processing. 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 36–41. https://doi.org/10.23919/SPA50552.2020.9241283
Hou, Y., Liu, R., Shu, M., Xie, X., & Chen, C. (2023). Deep Neural Network Denoising Model Based on Sparse Representation Algorithm for ECG Signal. IEEE Transactions on Instrumentation and Measurement, 72, 1–11. https://doi.org/10.1109/TIM.2023.3251408
Jinseok Lee, McManus, D. D., Merchant, S., & Chon, K. H. (2012). Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches. IEEE Transactions on Biomedical Engineering, 59(6), 1499–1506. https://doi.org/10.1109/TBME.2011.2175729
Karacan, I., Topkara Arslan, B., Karaoglu, A., Aydin, T., Gray, S., Ungan, P., & Türker, K. S. (2023). Estimating and minimizing movement artifacts in surface electromyogram. Journal of Electromyography and Kinesiology, 70, 102778. https://doi.org/10.1016/j.jelekin.2023.102778
Khalili, M., GholamHosseini, H., Lowe, A., & Kuo, M. M. Y. (2024). Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques. Medical & Biological Engineering & Computing, 62(12), 3599–3622. https://doi.org/10.1007/s11517-024-03165-1
Lahmiri, S. (2014). Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthcare Technology Letters, 1(3), 104–109. https://doi.org/10.1049/htl.2014.0073
Lazaro, J., Reljin, N., Hossain, M. B., Noh, Y., Laguna, P., & Chon, K. H. (2020). Wearable Armband Device for Daily Life Electrocardiogram Monitoring. IEEE Transactions on Biomedical Engineering, 67(12), 3464–3473. https://doi.org/10.1109/TBME.2020.2987759
Lazeta, L., Markovic, I., & Simovic, V. (2021). IIR filters designed for comparison and minimum-order design exploration using Matlab. 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), 875–879. https://doi.org/10.23919/MIPRO52101.2021.9596760
Li, H., & Boulanger, P. (2021). An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals. Sensors, 21(24), 8169. https://doi.org/10.3390/s21248169
Liu, J., Zhou, X., Liu, X., Wang, X., & Zhou, J. (2023). A Rhythm-Specific ECG Signal Quality Assessment Framework for Robust Cardiac Health Monitoring of AI-based Arrhythmia Classifer. 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), 1–5. https://doi.org/10.1109/BioCAS58349.2023.10388946
Ma, M., Du, M., Feng, Q., & Xiahou, S. (2024). A new particle filter algorithm filtering motion artifact noise for clean electrocardiogram signals in wearable health monitoring system. Review of Scientific Instruments, 95(1). https://doi.org/10.1063/5.0153241
Mahdavi, M. (2024). Complex-Domain FIR Filter Design for Signal Processing Applications. 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON), 180–185. https://doi.org/10.1109/MELECON56669.2024.10608765
Malleswari, P. N., Hima Bindu, Ch., & Satya Prasad, K. (2021). An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 51, 117–129. https://doi.org/10.4028/www.scientific.net/JBBBE.51.117
Mohd Apandi, Z. F., Ikeura, R., Hayakawa, S., & Tsutsumi, S. (2020). An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance. Bioengineering, 7(2), 53. https://doi.org/10.3390/bioengineering7020053
Mohguen, W., & Bouguezel, S. (2021). Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition. Engineering, Technology & Applied Science Research, 11(5), 7536–7541. https://doi.org/10.48084/etasr.4302
Pandey, V. K. (2010). Adaptive filtering for baseline wander removal in ECG. Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, 1–4. https://doi.org/10.1109/ITAB.2010.5687642
Reddy, V. V., M.Neelaveni, M.Sandeep, Ahmad, Sk. K., & Krishna, P. V. (2023). Comparison of FIR and IIR filters using ECG signal with different sampling frequencies. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/IRJMETS35977
S, S., & Sharma, R. (2025). Wavelets and multiresolution processing: theory and applications in signal and image analysis. ICTACT Journal on Image and Video Processing, 16(1), 3641–3646. https://doi.org/10.21917/ijivp.2025.0514
Saha, S., & Barman Mandal, S. (2024). FPGA implementation of IIR elliptic filters for de-noising ECG signal. Biomedical Signal Processing and Control, 96, 106544. https://doi.org/10.1016/j.bspc.2024.106544
Sharma, V. (2024). Design and Implementation of Efficient IIR Low Pass Filter Based On Vedic Multiplier Algorithm. International Journal for Research in Applied Science and Engineering Technology, 12(1), 115–117. https://doi.org/10.22214/ijraset.2024.57881
Shen, J., Li, X., Wang, Y., Li, Y., Bian, J., Zhu, X., He, X., & Li, J. (2024). Anti-Motion Interference Electrocardiograph Monitoring System: A Review. IEEE Sensors Journal, 24(10), 15727–15747. https://doi.org/10.1109/JSEN.2024.3383872
Skoric, J., D’Mello, Y., & Plant, D. V. (2024). A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography. IEEE Journal of Translational Engineering in Health and Medicine, 12, 348–358. https://doi.org/10.1109/JTEHM.2024.3368291
Steinberg, J. S., Varma, N., Cygankiewicz, I., Aziz, P., Balsam, P., Baranchuk, A., Cantillon, D. J., Dilaveris, P., Dubner, S. J., El-Sherif, N., Krol, J., Kurpesa, M., La Rovere, M. T., Lobodzinski, S. S., Locati, E. T., Mittal, S., Olshansky, B., Piotrowicz, E., Saxon, L., … Piotrowicz, R. (2017). 2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry. Heart Rhythm, 14(7), e55–e96. https://doi.org/10.1016/j.hrthm.2017.03.038
Vanchak, V., & Melnychuk, S. (2024). Discrete wavelet transform denoising method efficiency evaluation for processing pulse signals with harmonic components. Scientific Journal of the Ternopil National Technical University, 116(4), 124–134. https://doi.org/10.33108/visnyk_tntu2024.04.124
WHO. (2025, July 31). Cardiovascular diseases (CVDs). WHO. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Xie, X., Liu, H., Shu, M., Zhu, Q., Huang, A., Kong, X., & Wang, Y. (2021). A multi-stage denoising framework for ambulatory ECG signal based on domain knowledge and motion artifact detection. Future Generation Computer Systems, 116, 103–116. https://doi.org/10.1016/j.future.2020.10.024
Zeppenfeld, K., Tfelt-Hansen, J., de Riva, M., Winkel, B. G., Behr, E. R., Blom, N. A., Charron, P., Corrado, D., Dagres, N., de Chillou, C., Eckardt, L., Friede, T., Haugaa, K. H., Hocini, M., Lambiase, P. D., Marijon, E., Merino, J. L., Peichl, P., Priori, S. G., … Slade, A. (2022). 2022 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. European Heart Journal, 43(40), 3997–4126. https://doi.org/10.1093/eurheartj/ehac262
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