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Ideal Filtering Approach on DCT Domain for Biomedical Signals: Index Blocked DCT Filtering Method (IB-DCTFM)

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Abstract

We proposed Index-Blocked Discrete Cosine Transform Filtering Method (IB-DCTFM) to design ideal frequency range filter on DCT domain for biomedical signal which frequently exposed to specific frequency noise such as motion artifacts and 50/60 Hz powerline interference. IB-DCTFM removes unwanted frequency range signal on time domain by blocking specific DCT index on DCT domain. In simulation, electrocardiography, electromyography, photoplethysmography are used as a signal source and FIR, IIR and adaptive filter are used for comparison with proposed IB-DCTFM. To evaluate filter performance, we calculated signal-to-noise ratio and correlation coefficient to clean signal of each signal and filtering method respectively. As a result of filter simulation, average signal to noise ration and correlation coefficient of IB-DCTFM are improved about 75.8 dB/0.477, and FIR, IIR and adaptive filtering results are 24.8 dB/0.130, 54.3 dB/0.440 and 29.5 dB/0.200 respectively.

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References

  1. Tompkins, W. J., Biomedical digital signal processing: C language examples and laboratory experiments for the IBM PC. Prentice Hall PTR: Englewood Cliffs, NJ, USA, 1993.

    Google Scholar 

  2. Philips, W., Adaptive noise removal from biomedical signals using warped polynomials. IEEE Trans. Biomed. Eng. 43:480–492, 1996. doi:10.1109/10.488796.

    Article  Google Scholar 

  3. Amit, J. N., and Tompkins, W. J., EMD-based 60-Hz noise filtering of the ECG. Proc of the 29th Annual Int Conf of the IEEE EMBS, Cité Internationale, Lyon, France, August 23–26, 2007.

  4. Rangaraj, M. R., Biomedical signal analysis: A case-study approach. IEEE Press Series on Biomedical Engineering: Wiley, NY, USA, 2002.

    Google Scholar 

  5. Wu, Y., Rangayyan, R. M., et al., Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med. Eng. Phys. 31 (1)17–26, 2009. doi:10.1016/j.medengphy.2008.03.004.

    Article  Google Scholar 

  6. Pei, S. C., and Tseng, C. C., Adaptive IIR notch filter based on least mean p-power error criterion. IEEE Trans. Circuits Syst. 2 Analog Digit. Signal Process. 40 (8)525–528, 1993. doi:10.1109/82.242343.

    Article  Google Scholar 

  7. Zhang, D., Wavelet approach for ECG baseline wander correction and noise reduction. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2:1212–1215, 2005.

    Google Scholar 

  8. Kim, S. H., Ryoo, D. W. et al., Adaptive noise cancellation using accelerometers for the PPG signal from forehead. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2564–2567, 2007. doi:10.1109/IEMBS.2007.4352852.

  9. Laguna, P., and Sornmo, L., Introduction. Editorial on Signal processing in vital rhythms and signs. Philos. Trans. A. 367:207–211, 2009. doi:10.1098/rsta.2008.0239.

    Article  Google Scholar 

  10. Oppenheim, A.V., Schafer, R. S., and Buck, J. R., Discrete-time signal processing, 2nd edition. Prentice Hall, Upper Sandle River, NJ, 1999.

    Google Scholar 

  11. Shamir, M., Eidelman, L. A., et al., Pulse oximetry plethysmographic waveform during changes in blood volume. Br. J. Anaesth. 82 (2)178–181, 1999.

    Google Scholar 

  12. Zhang, W., Wang, X., et al., Noise reduction in ECG signal based on adaptive wavelet transform. Conf. Proc. IEEE Eng. Med. Biol. Soc. 3:2699–2702, 2005.

    Google Scholar 

  13. Ziarani, A. K., Konrad, A., Edward, S., and Rogers, Sr., A nonlinear adaptive method of elimination of power line interference in ECG signals. IEEE Trans. Biomed. Eng. 49:540–547, 2002. doi:10.1109/TBME.2002.1001968.

    Article  Google Scholar 

  14. Lin, Y. D., and Hu, Y. H., Power-line interference detection and suppression in ECG signal processing. IEEE Trans. Biomed. Eng. 55 (1)354–357, 2008. doi:10.1109/TBME.2007.902234.

    Article  MathSciNet  Google Scholar 

  15. Akay, M., Biomedical signal processing. Academic: San Diego, CA, 1994.

    Google Scholar 

  16. Akay, M., Wavelets in biomedical engineering. Ann. Biomed. Eng. 23 (5)531–542, 1995. doi:10.1007/BF02584453.

    Article  Google Scholar 

  17. Foo, J. Y. A., and Wilson, S. J., A computational system to optimise noise rejection in photoplethysmography signals during motion or poor perfusion states. Med. Biol. Eng. Comput. 44:140–145, 2006. doi:10.1007/s11517-005-0008-y.

    Article  Google Scholar 

  18. Wood, L. B., and Asada, H. H., Noise cancellation model validation for reduced motion artifact wearable PPG sensors using MEMS accelerometers. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1:3525–3528, 2006.

    Article  Google Scholar 

  19. Hoffer, J. A., and Loeb, G. E., Implantable electrical and mechanical interfaces with nerve and muscle. Ann. Biomed. Eng. 8 (4-6)351–360, 1980. doi:10.1007/BF02363438.

    Article  Google Scholar 

  20. Ortolan, R. L., Mori, R. N., et al., Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment. IEEE Trans. Neural Syst. Rehabil. Eng. 11 (1)60–69, 2003. doi:10.1109/TNSRE.2003.810432.

    Article  Google Scholar 

  21. Zhang, Y. T., Parker, P. A., and Scott, R. N., Signal-to-noise ratios of the myoelectric channel with additive noise. Proc. 19th Ann. Intern. Conf. IEEE Eng. Med. Biol. Soc. 4 (30)1582–1584, 1997.

    Google Scholar 

  22. Bazhyna, A., Gotchev, A., Christov, I. I., Daskalov, I. K., and Egiazarian, K., Beat-to-beat noise removal in noninvasive His-bundle electrocardiogram. Med. Biol. Eng. Comput. 42 (5)712–719, 2004. doi:10.1007/BF02347555.

    Article  Google Scholar 

  23. Nikolaev, N., Gotchev, A., Egiazarian, K., and Nikolov, Z., Suppression of electromyogram interference on the electrocardiogram by transform domain denoising. Med. Biol. Eng. Comput. 39 (6)649–655, 2001. doi:10.1007/BF02345437.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul, South Korea

    Hang Sik Shin, Chungkeun Lee & Myoungho Lee

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  1. Hang Sik Shin
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  2. Chungkeun Lee
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  3. Myoungho Lee
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Correspondence to Myoungho Lee.

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Shin, H.S., Lee, C. & Lee, M. Ideal Filtering Approach on DCT Domain for Biomedical Signals: Index Blocked DCT Filtering Method (IB-DCTFM). J Med Syst 34, 741–753 (2010). https://doi.org/10.1007/s10916-009-9289-2

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  • DOI: https://doi.org/10.1007/s10916-009-9289-2

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