Optimal Selection of Wavelet Transform for De-noising of ECG Signal on the Basis of Statistical Parameters

  • Shivani Saxena
  • Ritu Vijay
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)


The purpose of this work is to present a discrete wavelet transform-based de-noising procedure for ECG signal processing. The proposed method is applied on various wavelet transform to analyze waveform of noisy ECG signals and remove high-frequency noise with correction of baseline drift. In order to illustrate the quality and performance of the method, extraction of statistical parameters, including standard deviation, mean square error, signal-to-noise ratio and retained energy, from synthesized signal is done. From the obtained results, it was found that DWT-based de-noising technique preserves the dynamic and morphological features of ECG signal while suppressed noise components to some extent. The method is trained on ECG signal taken from MIT-BIH arrhythmia database from PhysioNet.


High-frequency noise ECG Discrete wavelet transforms Standard deviation Mean square error MATLAB 


  1. 1.
    Sundnes, J., Lines, G.T., Grottum, P., Tveito, A.: Electrical Activity in the Human Heart. Springer, Berlin, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Acharya, U.: Rajendra: Advances in Cardiac Signal Processing. Springer, Biocom Technologies (2007)CrossRefGoogle Scholar
  3. 3.
    Saritha, C., Sukanya, V., Narasimha Murthy, Y.: ECG signal analysis using wavelet transforms. Bulg. J. Phys. 35, 68–77 (2008)zbMATHGoogle Scholar
  4. 4.
    Tan, H.G.R., Tan, A.C., Khong, P.Y., Mok, V.H.: Best wavelet function identification system for ECG signal de-noise applications. In: International Conference on Intelligent and Advanced Systems, pp. 631–634 (2007)Google Scholar
  5. 5.
    Feher, A.: De-noising ECG signals by applying discrete wavelet transform. In: International Conference on Optimization of Electrical and Electronic Equipment & International Aegean Conference on Electrical Machines and Power Electronics, pp. 863–868 (2017)Google Scholar
  6. 6.
    Castillo, E., Morales, D.P., García, A., Martínez-Martí, F., Parrilla, L., Palma, A.J.: Noise suppression in ECG signals through efficient one-step wavelet processing techniques. J. Appl. Math. 491–498 (2013)Google Scholar
  7. 7.
    Usha Desai, C., Nayak, G.: Correction of baseline drift from the ECG by an efficient multi-resolution analysis algorithm. Int. J. Adv. Inf. Sci. Technol. 31, 5–9 (2014)Google Scholar
  8. 8.
    Nagendra, H., et al.: Wavelet based non linear thresholding techniques for pre processing ECG signals. Int. J. Biomed. Adv. Res. 08, 534–554 (2013)Google Scholar
  9. 9.
    Stantic, D., Ju, J.: Detecting abnormal ECG signals utilizing wavelet transform and standard deviation. Int. Sch. Sci. Res. Innov. 6(11), 544–550 (2012)Google Scholar
  10. 10.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shivani Saxena
    • 1
  • Ritu Vijay
    • 1
  1. 1.Department of Electronics Banasthali VidyapithNewai, TonkIndia

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