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Blind separation of ECG signals from noisy signals affected by electrosurgical artifacts

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Abstract

Electrocardiogram (ECG) signal monitoring is crucial in the operating room. During surgery, when using an electrosurgical unit (ESU), the ECG signal is corrupted by strong interference generated by the ESU, so-called Electrosurgical Artifacts (EAs). The objective of this study is to enhance the infected ECG signal and to remove the impact of the EAs. Motivated by the fact that the artifacts’ energy is greater than that of the ECG signal, we propose a new algorithm to select the intrinsic mode function (IMFs) based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) in combination with a normalized least mean square (NLMS) adaptive filter. More specifically, CEEMDAN is firstly applied to the observed ECG signals. Then, several IMFs are reconstructed and finally, the EAs and the de-noised ECG signal are estimated. To further improve the estimated ECG signal, a NLMS adaptive filter is applied by exploiting the noise estimated by the CEEMDAN-based algorithm. Our experimental results are obtained using real ECG signals. To illustrate the effectiveness of the proposed algorithm, we compare our results with those obtained by applying the thin singular value decomposition, an adaptive SVD or wavelets, all these methods are mixed with an adapter filter. The accuracy of all method is assessed using different performance metrics, such as MSE and improvement SNR. The proposed method outperforms all other methods. Indeed, it improves the SNR by an average of 7 dB and provides an ECG signal with a regular rhythm.

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Notes

  1. Sifting process is the subtraction operations necessary to extract an IMF.

  2. Mode mixing is the occurrence of oscillations of different amplitudes in one IMF, or the presence of similar oscillations in different IMFs.

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Authors and Affiliations

  1. Laboratory for the Analysis and Modeling of Random Phenomena (LAMPA), Department of Electronics, University of Mouloud Mammeri, Tizi Ouzou, BP 17 RP, 15000, Tizi Ouzou, Algeria

    Kahina Bensafia & Salah Haddab

  2. Lab-STICC, UMR6285 CNRS, ENSTA Bretagne, 2 Rue François Verny, 29806, Brest Cedex 9, France

    Kahina Bensafia, Ali Mansour & Benoit Clement

  3. Lanvéoc-Poulmic, Ecole Navale, BP 600, 29240, Brest-Armées, France

    Abdel-Ouahab Boudraa

  4. Military Teaching Hospital “Clermont Tonnerre”, Brest 29200 and French Military Health Service Academy, Ecole du Val-de-Grâce, 75000, Paris, France

    Philippe Ariès

Authors
  1. Kahina Bensafia
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  2. Ali Mansour
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  3. Abdel-Ouahab Boudraa
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  4. Salah Haddab
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  5. Philippe Ariès
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  6. Benoit Clement
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Correspondence to Kahina Bensafia.

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The authors declare that they have no conflict of interest.

Ethical approval

All the signals used in this article were recorded by physicians in the operating room of Clermont Tonnerre military teaching hospital in Brest, France. A strict medical protocol was approved by the hospital’s ethics committee. Written informed consents were obtained preoperatively from the patients. All competent patients scheduled for surgical procedures using an ESU during the study period were included. Exclusion criteria were age < 18 years and patients with pacemakers or implantable cardioverter defibrillators.

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Bensafia, K., Mansour, A., Boudraa, AO. et al. Blind separation of ECG signals from noisy signals affected by electrosurgical artifacts. Analog Integr Circ Sig Process 104, 191–204 (2020). https://doi.org/10.1007/s10470-020-01674-1

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