Abstract
Thus, to effectively solve the problem of eliminating the ECG baseline drift, it is necessary to filter the ECG signal. In this study for solve the problem of eliminating the ECG baseline drift, various filters were used: a low-pass filters based on the forward and inverse discrete Fourier transform, Butterworth filter, median filter, Savitsky-Golay filter. The results obtained confirmed the efficiency of filtering harmonic noise based on forward and backward DFT on real data. The method makes it possible to implement a narrow-band stop filter in the range from 0 to the Nyquist frequency. In this case, the notch band can be less than 0.1% of the Nyquist frequency, which is important when processing ECG signals. The filtering result was evaluated by the appearance of the filtered ECG. The evaluation criterion was the presence on the signal of characteristic fragments reflecting the work of the atria and ventricles of the heart in the form of a P wave, a QRS complex and a ST-T segment. A new method has been developed for filtering the ECG signal, which is based on the use of a sliding window containing 5 points. A linear function is constructed from a sample of five points using the least squares method. The value of the resulting linear function at the midpoint is used as the new value. Several iterations are performed to achieve a good result. To eliminate the baseline drift for the ECG fragment, it is proposed to use a special cubic interpolation spline. The algorithm for constructing the used spline requires solving a system of equations with a tridiagonal matrix.
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References
Texas Instruments. Low-Power, 1-Channel, 24-Bit Analog Front-End for Biopotential Measurements. http://www.ti.com/lit/ds/symlink/ads1291.pdf
Bae, T., Lee, S., Kwon, K.: An adaptive median filter based on sampling rate for r-peak detection and major-arrhythmia analysis. Sensors 20(6144) (2020). https://doi.org/10.3390/s20216144
Blanco-Velasco, M., Weng, B., Barner, K.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38, 1–13 (2008). https://doi.org/10.1016/j.compbiomed.2007.06.003
Goldberger, A., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000). https://doi.org/10.1161/01.CIR.101.23.e215
Haider, S.I., Alhussein, M.: Detection and classification of baseline-wander noise in ECG signals using discrete wavelet transform and decision tree classifier. Elektronika Ir Elektrotechnika 25(4), 47–57 (2019). https://doi.org/10.5755/j01.eie.25.4.23970
Hao, W., Chen, Y., Xin, Y.: ECG baseline wander correction by mean-median filter and discrete wavelet transform. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, pp. 2712–2715 (2011). https://doi.org/10.1109/IEMBS.2011.6090744
Hargittai, S.: Savitzky-Golay least-squares polynomial filters in ECG signal processing. In: Computers in Cardiology Lyon, France, pp. 763–766 (2005). https://doi.org/10.1109/CIC.2005.1588216
Holmes, C., Fedewa, M., Winchester, L., Macdonald, H., Wind, S., Esco, M.: Validity of smartphone heart rate variability pre-and post-resistance exercise. Sensors 20, 5738 (2020). https://doi.org/10.3390/s20205738
Jagtap, S., Uplane, M.: The impact of digital filtering to ECG analysis: butterworth filter application. In: International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, India, pp. 1–6 (2012). https://doi.org/10.1109/ICCICT.2012.6398145
Krak, I., Pashko, A., Stelia, O., Barmak, O., Pavlov, S.: Selection parameters in the ECG signals for analysis of QRS complexes. In: Proceedings of the 1st International Workshop on Intelligent Information Technologies & Systems of Information Security, Khmelnytskyi, Ukraine, pp. 1–13 (2020). http://ceur-ws.org/Vol-2623/paper1.pdf
Krak, I., Stelia, O., Pashko, A., Efremov, M., Khorozov, O.: Electrocardiogram classification using wavelet transformations. In: IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, pp. 930–933 (2020). https://doi.org/10.1109/TCSET49122.2020.235573
Krak, I., Stelia, O., Pashko, A., Khorozov, O.: Physiological signals analysis, recognition and classification using machine learning algorithms. In: Proceedings of The Third International Workshop on Computer Modeling and Intelligent Systems (CMIS 2020), Zaporizhzhia, Ukraine, pp. 955–965 (2020)
Krak, I., Stelia, O., Potapenko, L.: Controlled spline of third degree: approximation properties and practical application. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds.) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. AISC, vol. 1020, pp. 215–224. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26474-1_16
Liu, M., Hao, H., Xiong, P., et al.: Constructing a guided filter by exploiting the Butterworth filter for ECG signal enhancement. J. Med. Biol. Eng. 38, 980–992 (2018). https://doi.org/10.1007/s40846-017-0350-1
Meyer, C., Keiser, H.: Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput. Biomed. Res. 10(5), 459–470 (1977). https://doi.org/10.1016/0010-4809(77)90021-0
Nahiyan, K., Amin, A.: Removal of ECG baseline wander using Savitzky-Golay filter based method. Bangladesh J. Med. Phys. 8(1), 32–45 (2017). https://doi.org/10.3329/bjmp.v8i1.33932
Pashko, A., Krak, I., Stelia, O., Khorozov, O.: Isolation of informative features for the analysis of QRS complex in ECG signals. In: Babichev, S., Lytvynenko, V., Wójcik, W., Vyshemyrskaya, S. (eds.) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2020. AISC, vol. 1246, pp. 409–422. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-54215-3_26
Savitzky, A.G.M.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964)
Sevakula, R., Au-Yeung, W., Singh, J., Heist, E., Isselbacher, E., Armoundas, A.: State-of-the-art machine learning techniques aiming to improve patient outcomes per-taining to the cardiovascular system. J. Am. Heart Assoc. 9(4), e013924 (2020). https://doi.org/10.1161/JAHA.119.013924
Shabaan, M., Arshid, K., Yaqub, M., et al.: Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis. BMC Med. Inform. Decis. Mak. 20(117) (2020). https://doi.org/10.1186/s12911-020-01199-7
Upganlawar, I., Chowhan, H.: Pre-processing of ECG signals using filters. Int. J. Comput. Trends Technol. (IJCTT) 11(4), 166–168 (2014). https://doi.org/10.14445/22312803/IJCTT-V11P1355
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Pashko, A., Krak, I., Stelia, O., Wojcik, W. (2022). Baseline Wander Correction of the Electrocardiogram Signals for Effective Preprocessing. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_34
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