Abstract
An essential tool for diagnosing heart diseases is the electrocardiogram (ECG) signal. The accuracy of the diagnosis is impacted by the noise that occurs while this signal is being acquired. Denoising turns into a foundational step in this context. DWT-ADTF is an effective ECG signal-denoising method. This work tries to provide an HW/SW co-design solution to this method. The signal is well denoised, according to the simulation results of the developed designs.
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References
Colominas, M.A., Schlotthauer, G., Torres, M.E.: Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed. Signal Process. Control. 14, 19-29 (2014). https://doi.org/10.1016/j.bspc.2014.06.009
Huang, W., Cai, N., Xie, W., Ye, Q., Yang, Z.: ECG baseline wander correction based on ensemble empirical mode decomposition with complementary adaptive noise. J. Med. Imaging Heal. Informatics. 5, 1796-1799 (2015). https://doi.org/10.1166/jmihi.2015.1647
Yao, L., Pan, Z.: A new method based CEEMDAN for removal of baseline wander and powerline interference in ECG signals. Optik (Stuttg). 223, 165566 (2020). https://doi.org/10.1016/j.ijleo.2020.165566
Cuomo, S., De Pietro, G., Farina, R., Galletti, A., Sannino, G.: A revised scheme for real time ECG Signal denoising based on recursive filtering. Biomed. Signal Process. Control. 27, 134-144 (2016). https://doi.org/10.1016/j.bspc.2016.02.007
Liu, M., Hao, H.Q., Xiong, P., Lin, F., Hou, Z.G., Liu, X.: 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
Singh, P., Pradhan, G.: Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering. Australas. Phys. Eng. Sci. Med. 41, 891-904 (2018). https://doi.org/10.1007/s13246-018-0685-0
Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El, O., Maoulainine, F.M.R.: ScienceDirect an efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Integr. Med. Res. 36, 499-508 (2016). https://doi.org/10.1016/j.bbe.2016.04.001
Singh, P., Pradhan, G., Shahnawazuddin, S.: Denoising of ECG signal by non-local estimation of approximation coefficients in DWT. Biocybern. Biomed. Eng. 37, 599-610 (2017). https://doi.org/10.1016/j.bbe.2017.06.001
Vargas, R.N., Veiga, A.C.P.: Electrocardiogram signal denoising by clustering and soft thresholding. IET Signal Process. 12, 1165-1171 (2018). https://doi.org/10.1049/iet-spr.2018.5162
Xiong, P., Wang, H., Liu, M., Lin, F., Hou, Z., Liu, X.: A stacked contractive denoising auto-encoder for ECG signal denoising. Physiol. Meas. 37, 2214-2230 (2016). https://doi.org/10.1088/0967-3334/37/12/2214
Wang, G., Yang, L., Liu, M., Yuan, X., Xiong, P., Lin, F., Liu, X.: ECG signal denoising based on deep factor analysis. Biomed. Signal Process. Control. 57, 101824 (2020). https://doi.org/10.1016/j.bspc.2019.101824
Kabir, M.A., Shahnaz, C.: Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control. 7, 481-489 (2012). https://doi.org/10.1016/j.bspc.2011.11.003
Boda, S., Mahadevappa, M., Kumar, P.: Biomedical signal processing and control a hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT. Biomed. Signal Process. Control. 67, 102466 (2021). https://doi.org/10.1016/j.bspc.2021.102466
Rakshit, M., Das, S.: Biomedical signal processing and control an efficient ecg denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomed. Signal Process. Control. 40, 140-148 (2018). https://doi.org/10.1016/j.bspc.2017.09.020
Jenkal, W., Latif, R., Toumanari, A., Elouardi, A., Hatim, A., El’bcharri, O.: Real-time hardware architecture of the adaptive dual threshold filter based ECG signal denoising. J. Theor. Appl. Inf. Technol. 96, 4649–4659 (2018)
Abdelhalim, M.B., Habib, S.E.D.: An integrated high-level hardware/software partitioning methodology. Des. Autom. Embed. Syst. 15, 19-50 (2011). https://doi.org/10.1007/s10617-010-9068-9
Shannon, L., Chow, P.: Leveraging reconfigurability in the hardware/software codesign process. ACM Trans. Reconfigurable Technol. Syst. 4, 2000840 (2011). https://doi.org/10.1145/2000832.2000840
Muck, T.R., Frohlich, A.A.: Toward unified design of hardware and software components using C++. IEEE Trans. Comput. 63, 2880-2893 (2014). https://doi.org/10.1109/TC.2013.159
Corporation, A.: Cyclone II Device Handbook , Volume 1 Preliminary Information. Innovation 1 (2008)
Mejhoudi, S., Latif, R., Jenkal, W., Saddik, A., El Ouardi, A.: Hardware architecture for adaptive dual threshold filter and discrete wavelet transform based ECG signal denoising. Int. J. Adv. Comput. Sci. Appl. 12, 45-54 (2021). https://doi.org/10.14569/IJACSA.2021.0121106
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Bendahane, B., Jenkal, W., Laaboubi, M., Latif, R. (2023). Hardware Software Co-design Approch for ECG Signal Analysis. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_18
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