COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES

The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

Keywords:

ECG signal, Motion Artifact, LMS algorithm, NLMS, PNLMS, IPNLMS, Wearable Devices

References

An, X., & Stylios, G. K. (2020). Comparison of motion artefact reduction methods and the implementation of adaptive motion artefact reduction in wearable electrocardiogram monitoring. Sensors (Switzerland), 20(5). https://doi.org/10.3390/s20051468

Banos, O., Garcia, R., Holgado-terriza, J. A., Damas, M., Pomares, H., Rojas, I., Saez, A., & Villalonga, C. (2014). mHealthDroid: a novel framework for agile development of mobile health applications. 91–98.

Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado-Terriza, J. A., Lee, S., Pomares, H., & Rojas, I. (2015). Design, implementation and validation of a novel open framework for agile development of mobile health applications. BioMedical Engineering Online, 14(2), 1–20. https://doi.org/10.1186/1475-925X-14-S2-S6

Burns, A., Greene, B. R., McGrath, M. J., O’Shea, T. J., Kuris, B., Ayer, S. M., Stroiescu, F., & Cionca, V. (2010). SHIMMERTM - A wireless sensor platform for noninvasive biomedical research. IEEE Sensors Journal, 10(9), 1527–1534. https://doi.org/10.1109/JSEN.2010.2045498

Cömert, A., & Hyttinen, J. (2015). A motion artifact generation and assessment system for the rapid testing of surface biopotential electrodes. Physiological Measurement, 36(1), 1–25. https://doi.org/10.1088/0967-3334/36/1/1

Duttweiler, D. L. (2000). Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE Transactions on Speech and Audio Processing, 8(5), 508–517. https://doi.org/10.1109/89.861368

Ebrahimzadeh, E., Pooyan, M., Jahani, S., Bijar, A., & Setaredan, S. K. (2015). ECG signals noise removal: Selection and optimization of the best adaptive filtering algorithm based on various algorithms comparison. Biomedical Engineering - Applications, Basis and Communications, 27(4), 1–13. https://doi.org/10.4015/S1016237215500386

Friesen, G. M., Jannett, T. C., Yates, S. L., Quint, S. R., & Nagle, H. T. (1990). A Comparison of the Noise Sensitivity. IEEE. Transactions on Biomedical Engineering, 37(January).

Ghaleb, F. A., Kamat, M. B., Salleh, M., Rohani, M. F., & Razak, S. A. (2018). Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter. In PLoS ONE (Vol. 13, Issue 11). https://doi.org/10.1371/journal.pone.0207176

Ghaleb, F. A., Kamat, M., Salleh, M., Rohani, M. F., & Hadji, S. E. (2018). Motion artifact reduction algorithm using sequential adaptive noise filters and estimation methods for mobile ECG. Lecture Notes on Data Engineering and Communications Technologies, 5, 116–123. https://doi.org/10.1007/978-3-319-59427-9_13

Han, D., Bashar, S. K., Lázaro, J., Mohagheghian, F., Peitzsch, A., Nishita, N., Ding, E., Dickson, E. L., Dimezza, D., Scott, J., Whitcomb, C., Fitzgibbons, T. P., McManus, D. D., & Chon, K. H. (2022). A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia. Biosensors, 12(2). https://doi.org/10.3390/bios12020082

Huang, B., & Kinsner, W. (2002). ECG frame classification using dynamic time warping. Canadian Conference on Electrical and Computer Engineering, 2, 1105–1110. https://doi.org/10.1109/ccece.2002.1013101

Huang, M., Chen, D., & Xiong, F. (2019). An effective adaptive filter to reduce motion artifacts from ECG signals using accelerometer. ACM International Conference Proceeding Series, 83–88. https://doi.org/10.1145/3326172.3326214

Jung, H.-K., & Jeong, D.-U. (2013). Development of wearable ECG measurement system using EMD for motion artifact removal.

Kim, H., Kim, S., Van Helleputte, N., Berset, T., Geng, D., Romero, I., Penders, J., Van Hoof, C., & Yazicioglu, R. F. (2012). Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system. 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications, July 2014, 160–163. https://doi.org/10.1109/BioCAS.2012.6418472

Lee, J. W., & Yun, K. S. (2017). ECG monitoring garment using conductive carbon paste for reduced motion artifacts. Polymers, 9(9), 1–14. https://doi.org/10.3390/polym9090439

Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: A review of the subtraction procedure. BioMedical Engineering Online, 4, 1–18. https://doi.org/10.1186/1475-925X-4-50

Lilienthal, J., & Dargie, W. (2021). Comparison of Reference Sensor Types and Position for Motion Artifact Removal in ECG. European Signal Processing Conference, 2021-Augus, 1296–1300. https://doi.org/10.23919/EUSIPCO54536.2021.9616221

Liu, Y., & Pecht, M. G. (2011). Reduction of motion artifacts in electrocardiogram monitoring using an optical sensor. Biomedical Instrumentation and Technology, 45(2), 155–163. https://doi.org/10.2345/0899-8205-45.2.155

Mandala, S., Fuadah, Y. N., Arzaki, M., & Pambudi, F. E. (2017). Performance analysis of wavelet-based denoising techniques for ECG signal. 2017 5th International Conference on Information and Communication Technology, ICoIC7 2017, 0(c). https://doi.org/10.1109/ICoICT.2017.8074701

Medina, A., Lopez, N., Galdos, J., Supo, E., Rendulich, J., & Sulla, E. (2022). Continuous Blood Pressure Estimation in Wearable Devices Using Photoplethysmography: A Review. International Journal of Emerging Technology and Advanced Engineering, 12(10), 104–113. https://doi.org/10.46338/ijetae1022_12

Meyer, C. R., & Keiser, H. N. (1977). Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Computers and Biomedical Research, 10(5), 459–470. https://doi.org/10.1016/0010-4809(77)90021-0

Milanesi, M., Martini, N., Vanello, N., Positano, V., Santarelli, M. F., & Landini, L. (2008). Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals. Medical and Biological Engineering and Computing, 46(3), 251–261. https://doi.org/10.1007/s11517-007-0293-8

Raya, M. A. D., & Sison, L. G. (2002). Adaptive noise cancelling of motion artifact in stress ECG signals using accelerometer. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2, 1756–1757. https://doi.org/10.1109/iembs.2002.1106637

Seok, D., Lee, S., Kim, M., Cho, J., & Kim, C. (2021). Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems. Frontiers in Electronics, 2(May), 1–17. https://doi.org/10.3389/felec.2021.685513

Slock, D. T. M. (1993). On the Convergence Behavior of the LMS and the Normalized LMS Algorithms. IEEE Transactions on Signal Processing, 41(9), 2811–2825. https://doi.org/10.1109/78.236504

Sörnmo, L., & Laguna, P. (2005). Bioelectrical Signal Processing in Cardiac and Neurological Applications (1st ed.). Elsevier Inc.

Sultana, N., Kamatham, Y., & Kinnara, B. (2015). Performance analysis of adaptive filtering algorithms for denoising of ECG signals. 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 297–302. https://doi.org/10.1109/ICACCI.2015.7275624

Tejaswi, V., Surendar, A., & Srikanta, N. (2020). Simulink implementation of RLS algorithm for resilient artefacts removal in ECG signal. International Journal of Advanced Intelligence Paradigms, 16(3–4), 324–337. https://doi.org/10.1504/IJAIP.2020.107529

Thakor, N. V., & Zhu, Y. S. (1991). Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. IEEE Transactions on Biomedical Engineering, 38(8), 785–794. https://doi.org/10.1109/10.83591

Tuzcu, V., & Nas, S. (2005). Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 1, 182–186. https://doi.org/10.1109/icsmc.2005.1571142

Van Alsté, J. A., & Schilder, T. S. (1985). Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps. IEEE Transactions on Biomedical Engineering, BME-32(12), 1052–1060. https://doi.org/10.1109/TBME.1985.325514

Widrow, B., Williams, C. S., Glover, J. R., McCool, J. M., Hearn, R. H., Zeidler, J. R., Kaunitz, J., Dong, E., & Goodlin, R. C. (1975). Adaptive Noise Cancelling: Principles and Applications. Proceedings of the IEEE, 63(12), 1692–1716. https://doi.org/10.1109/PROC.1975.10036

Wittenmark, B. (2014). Adaptive filter theory. Simon Haykin. In Automatica (Vol. 29, Issue 2). https://doi.org/10.1016/0005-1098(93)90162-M

Xiong, F., Chen, D., Chen, Z., & Dai, S. (2019). Cancellation of motion artifacts in ambulatory ECG signals using TD-LMS adaptive filtering techniques. Journal of Visual Communication and Image Representation, 58, 606–618. https://doi.org/10.1016/j.jvcir.2018.12.030

Xiong, F., Chen, D., & Huang, M. (2020). A wavelet adaptive cancellation algorithm based on multi‐inertial sensors for the reduction of motion artifacts in ambulatory ECGs. Sensors (Switzerland), 20(4). https://doi.org/10.3390/s20040970

Xu, P., Tao, X., & Wang, S. (2011). Measurement of wearable electrode and skin mechanical interaction using displacement and pressure sensors. Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, 2, 1131–1134. https://doi.org/10.1109/BMEI.2011.6098433

Yadav, S., Saha, S. K., Kar, R., & Mandal, D. (2021). Optimized adaptive noise canceller for denoising cardiovascular signal using SOS algorithm. Biomedical Signal Processing and Control, 69(March), 102830. https://doi.org/10.1016/j.bspc.2021.102830

Yoon, S. W., Min, S. D., Yun, Y. H., Lee, S., & Lee, M. (2008). Adaptive motion artifacts reduction using 3-axis accelerometer in E-textile ECG measurement system. Journal of Medical Systems, 32(2), 101–106. https://doi.org/10.1007/s10916-007-9112-x

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

COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES

Issue Vol. 20 No. 1 (2024)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 20 No. 1 (2024)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License