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  • Research Article
  • Open Access

Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

EURASIP Journal on Advances in Signal Processing20072007:041274

  • Received: 7 May 2006
  • Accepted: 11 January 2007
  • Published:


We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the -function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.


  • Quantum Information
  • Quantitative Evaluation
  • Center Frequency
  • Interference Effect
  • Noise Reduction

Authors’ Affiliations

Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, P.O. Box 11365-9363, Tehran, Iran


  1. Frankiewicz Z, Piêtka E: Komputerowe eliminacja linii izoelektrycznej z sygnau EKG. Problemy Techniki Medycznej 1985.,16(1):Google Scholar
  2. Thakor NV, Zhu Y-S: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Transactions on Biomedical Engineering 1991,38(8):785-794. 10.1109/10.83591View ArticleGoogle Scholar
  3. Laguna P, Jane R, Meste O, et al.: Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques. IEEE Transactions on Biomedical Engineering 1992,39(10):1032-1044. 10.1109/10.161335View ArticleGoogle Scholar
  4. Moody GB, Mark RG: QRS morphology representation and noise estimation using the Karhunen-Loève transform. Proceedings of Computers in Cardiology, September 1989, Jerusalem, Israel 269–272.Google Scholar
  5. Barros AK, Mansour A, Ohnishi N: Removing artifacts from electrocardiographic signals using independent components analysis. Neurocomputing 1998,22(1–3):173-186.View ArticleGoogle Scholar
  6. He T, Clifford GD, Tarassenko L: Application of ICA in removing arte facts from the ECG. to appear in Neural Processing LettersGoogle Scholar
  7. Clifford GD, Tarassenko L: One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats. Electronics Letters 2001,37(18):1126-1127. 10.1049/el:20010762View ArticleGoogle Scholar
  8. Daqrouq K: ECG baseline wander reduction using discrete wavelet transform. Asian Journal of Information Technology 2005,4(11):989-995.Google Scholar
  9. Kestler HA, Haschka M, Kratz W, et al.: De-noising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter. Proceedings of Computers in Cardiology, September 1998, Cleveland, Ohio, USA 233–236.Google Scholar
  10. Donoho DL: De-noising by soft-thresholding. IEEE Transactions on Information Theory 1995,41(3):613-627. 10.1109/18.382009MathSciNetView ArticleGoogle Scholar
  11. Popescu M, Cristea P, Bezerianos A: High resolution ECG filtering using adaptive BSayesian wavelet shrinkage. Proceedings of Computers in Cardiology, September 1998, Cleveland, Ohio, USA 401–404.Google Scholar
  12. Agante Da Silva PMG, Marques De Sá JP: ECG noise filtering using wavelets with soft-thresholding methods. Proceedings of Computers in Cardiology, September 1999, Hannover, Germany 535–538.Google Scholar
  13. Meyer CR, Keiser HN: Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Computers and Biomedical Research 1977,10(5):459-470. 10.1016/0010-4809(77)90021-0View ArticleGoogle Scholar
  14. MacFarlane PW, Peden J, Lennox J, Watts MP, Lawrie TD: The Glasgow system. In Trends in Computer-Processed Electrocardiograms: Proceedings of the IFIP Working Conference on Trends in Computer-Processed Electrocardiograms. North-Holland, New York, NY, USA; 1977:143-150.Google Scholar
  15. Van Alste JA, Schilder TS: 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 1985,32(12):1052-1060.View ArticleGoogle Scholar
  16. Mozaffary B, Tinati MA: ECG baseline wander elimination using wavelet packets. Transactions on Engineering, Computing and Technology 2004, 3: 22–24.Google Scholar
  17. Yao J, Zhang YT: Bionic wavelet transform: a new time-frequency method based on an auditory model. IEEE Transactions on Biomedical Engineering 2001,48(8):856-863. 10.1109/10.936362View ArticleGoogle Scholar
  18. Yao J, Zhang YT: Cochlear is an inhomogeneous, active and nonlinear model. Proceedings of the 1st Joint Meeting of BMES & IEEE/EMBS, October 1999, Atlanta, Ga, USA 1031.Google Scholar
  19. Yao J, Zhang YT: From otoacoustic emission modeling to bionic wavelet transform. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2000, Chicago, Ill, USA 1: 314–316.Google Scholar
  20. Yao J, Zhang YT: The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations. IEEE Transactions on Biomedical Engineering 2002,49(11):1299-1309. 10.1109/TBME.2002.804590View ArticleGoogle Scholar
  21. Yuan X: Auditory model-based bionic wavelet transform for speech enhancement, M.S. thesis. Speech and Signal Processing Laboratory, Marquette University, Milwaukee, Wis, USA; 2003.Google Scholar
  22. Donoho DL, Johnstone IM: Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association 1995,90(432):1200-1224. 10.2307/2291512MathSciNetView ArticleGoogle Scholar
  23. Park KL, Lee KJ, Yoon HR: Application of a wavelet adaptive filter to minimise distortion of the ST-segment. Medical and Biological Engineering and Computing 1998,36(5):581-586. 10.1007/BF02524427View ArticleGoogle Scholar
  24. The MIT-BIH Arrhythmia Database
  25. McSharry PE, Clifford GD, Tarassenko L, Smith LA: A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 2003,50(3):289-294. 10.1109/TBME.2003.808805View ArticleGoogle Scholar
  26. McSharry PE, Clifford GD: ECGSYN—a realistic ECG waveform generator.


© O. Sayadi and M. B. Shamsollahi. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.