Adaptive algorithms for real-time filtering of electrocardiogram with multilevel noise estimation

Authors

  • Н.О. Тулякова
  • О.М. Трофимчук

DOI:

https://doi.org/10.30837/rt.2020.2.201.20

Keywords:

ECG signal, non-stationary noise, EMG noise, adaptive filtering in real time mode

Abstract

Non-stationary noise with a varying and previously unknown level of variance in time, an example of which is electromyographic (EMG) noise, often contaminates a signal of an electrocardiogram (ECG). Ensuring high quality filtering of non-stationary noise in the ECG is necessary, since its presence significantly reduces the accuracy of measurements, complicates or makes it impossible to use recognition and classification algorithms and making reliable diagnostic decisions, accordingly. An adaptive method of suppression of non-stationary noise in ECG with noise and signal-dependent switching of component filters by increasing the number of noise levels for their estimation has been further developed in this study. On the basis of the method improved in this way, the one-, two-, and three-pass algorithms and an algorithm with re-filtering dependent on preliminary noise estimates are designed. As components of the method, it is proposed to use simple and optimal Savitzky-Golay and moving averaging filters. Statistical estimates of efficiency are obtained by numerical simulations using such criteria as mean-square error and signal-to-noise ratio for a ECG model signal sampled at 1 kHz and the output signals of the adaptive algorithms under different additive Gaussian noise conditions have shown improvements in filtering quality. There are no distortions in the QRS complex at very low noise. The high efficiency of noise suppression at different levels of its variance and the advantage of re-filtering are demonstrated. The filter applying to the ECG signal with the real EMG noise confirms high quality of non-stationary noise suppression and of ECG waves preservation and the advantage over high-effective dynamic filtering also. The proposed adaptive algorithms do not require complex computational operations and are quick, i.e. allow real-time signal processing (with little delay in obtaining a current sample of output signal with respect to the corresponding sample of the signal at the input of filtering algorithm).

References

De Luca C. J. Physiology and mathematics of myoelectric signals // IEEE Trans. Biomed. Eng. 1979. V. 26. P. 313 – 325.

Christov I. I., Daskalov I. K. Filtering of electromyogram artifacts from the electrocardiogram // Medical Engineering & Physics. 1999. V. 21. P. 731 – 736.

Gotchev A., Christov I., Egiazarian K. Denoising of electrocardiogram from electromyogram artifacts by combined transform-domain and dynamic approximation method // ICASSP’2002: Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing. 2002. P. 3872 – 3875. doi: 10.1109/ICASSP.

Zhang Y. T., Rangayyan R. M., Frank C. B., Bell G. D. Adaptive cancellation of muscle contraction interference from knee joint vibration signals // IEEE Trans. Biomed. Eng. 1994. V. 41, No. 2. P. 181 – 191.

De Luca C. J., Hostage E. C. Relationship between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions // J. Neurophysiol. 2010. V. 104, No. 2. P. 1034 – 1046.

Goodgold J. and Eberstein A. Electrodiagnosis of Neuromuscular Diseases. 3rd ed. Baltimore, MD: Williams and Wilkins, 1983.

Widrow B., Glover Jr. J. R., McCool J. M., Kaunitz J., Williams C. S., Hearn R. H., Zeidler J. R., Dong Jr. E., Goodling R. C. Adaptive noise canceling: Principles and applications // Proc.IEEE. 1975. V. 63, No. 12. P. 1692 – 1716.

Drake J. D. M., Callaghan J. P. Elimination of electrocardiogram contamination from electromyogram signals: an evaluation of currently used removal techniques // J. Electromyogr. Kinesiol. 2006. V. 16. P. 175 – 187.

Christov I., Raikova R., Angelova S. Separation of electrocardiographic from electromyographic signals using dynamic filtration // Medical Engineering & Physics. 2018. V. 57. P. 1 – 10. doi: 10.1016/j.medengphy.2018.04.007.

Christov I. I., Neycheva T. D., Raikova R. T. ECG-noise Removal from EMG-signal by Subtraction of Hybrid Template of Averaged P-QRS-T Intervals // Proc. XXVIII Int. Sc. Conf. Electronics ET2019. 2019. doi: 10.1109/ET.2019.8878620

Tompkins W. J. Biomedical Digital Signal Processing. Upper Saddle River, NJ: Prentice Hall, 1995.

Challis R. E., Kitney R. I. Biomedical signal processing: Part 1.Time-domain methods // Med. Biol. Eng. Comput. 1990. V. 28. P. 509 – 524.

Rangayyan R. M. Biomedical signal analysis. A case-Study Approach. Wiley IEEE Press, 2002.

Oktem H., Nikolaev N., Gotchev A., Egiazarian K. ECG denoising approach aimed at detail preservation // Biosignal' 2000: Proc. of the Int. Conf. 2000. P. 26 – 29.

Nikolaev N., Gotchev A. ECG signal denoising using wavelet domain Wiener filtering // EUSIPCO-2000: Proc. of the European Signal Processing Conf. 2000. P. 51 – 54.

Gotchev A. Spline and Wavelet Based Techniques for Signal and Image Processing. Thesis for the degree of Doctor of Technology, 5th September, Tampere (Finland), Tampere University of Technology Publ., 2003. 171 p.

Dotsinsky I., Mihov G. Simple approach for tremor suppression in electrocardiograms // Int. J. Bioaotomation. 2010. V. 14, No. 2. P. 129 – 138.

Bortolan G., Christov I., Simova I., Dotsinsky I. Noise processing in exercise ECG stress test for the analysis and the clinical characterization of QRS and T wave alternans // Biomedical Signal Processing and Control. 2015. V. 18. P. 378 – 385.

Savitzky A., Golay M. Smoothing and differentiation of data by simplified least squares procedures // Analytical Chemistry. 1964. V. 36. P. 1627 – 1639. doi: 10.1021/ac60214a047.

Bortolan G., Christov I. Dynamic filtration of high-frequency noise in ECG signal // Computing in Cardiology. 2014. V. 41. P. 1089 – 1092.

Christov I., Neycheva T., Schmid R., Stoyanov T., Abächerli R. Pseudo real-time low-pass filter in ECG, self-adjustable to the frequency spectra of the waves // Medical & Biological Engineering & Computing. 2017. V. 55, No. 9. P. 1579 – 1588.

Christov I., Neycheva T., Schmid R. Fine tuning of the dynamic low-pass filter for electromyographic noise suppression in electrocardiograms // Computing in Cardiology. 2017. V. 44. P. 1 – 4.

Christov I., Gotchev A., Bortolan G., Neycheva T., Raikova R., Schmid R. Separation of the electromyographic from the electrocardiographic signals and vice versa. A topical review of the Dynamic procedure // Int. J. Bioaotomation. 2020. (in print).

Тулякова Н. О. Методы устранения миографического шума в электрокардиограмме // Радіоелектронні і комп’ютерні системи. 2014. № 2 (66). С. 85 – 92.

Тулякова Н. О. Локально-адаптивная мириадная фильтрация сигнала электрокардиограммы // Радіотехніка. 2015. Вип. 180. С. 152 – 162.

Тулякова Н.О., Трофимчук А.Н., Стрижак А.Е. Алгоритмы фильтрации электрокардиограммы с дина-мически изменяемым размером окна // Радіоелектронні і комп’ютерні системи. 2016. № 2 (76). С. 4 – 14.

Тулякова Н. О., Трофимчук А. Н., Стрижак А. Е. Адаптивные мириадные фильтры для обработки сигналов электрокардиограммы, регистрируемых с высокой частотой дискретизации // Радіоелектронні і комп’ютерні системи. 2016. № 4 (78). С. 97 – 107.

Tulyakova N. Locally-adaptive myriad filters for processing ECG signals in real time // Int. J. Bioaotomation. 2017. V. 21, No. 1. P. 5 – 18.

Tulyakova N., Trofimchuk A., Strizhak A. Adaptive algorithms for elimination of electromyographic noise in the electrocardiogram signal // Telecommunications and Radio Engineering. 2018. V. 77, No. 6. P. 549 – 561. doi: 10.1615/TelecomRadEng.v77.i6.70.

Tulyakova N., Neycheva T., Trofymchuk O., Stryzhak O. Locally-adaptive myriad filtration of one-dimensional complex signal // Int. J. Bioaotomation. 2018. V. 22, No. 3. P. 273 – 294.

Тулякова Н. О., Трофимчук А. Н., Стрижак А. Е. Модифицированные локально-адаптивные мириадные фильтры // Радіотехніка. 2019. Вип.196. С. 77 – 88.

Тулякова Н. О., Трофимчук А. Н., Стрижак А. Е. Адаптивный метод с шумо- и сигнально-зависимым переключением фильтров для подавления нестационарного шума в сигнале электрокардиограммы в реальном времени // Радіотехніка. 2018. Вип.194. С. 79 – 96.

Davies L., Gather U. The identification of multiple outliers // J. American Statistical Assoc. 1993. No. 88. P. 782 – 801.

Pearson R.K., Neuvo Y., Astola J. The Class of Generalized Hampel Filters// EUSIPCO. 2015. P.2546-2550.

Astola J., Kuosmanen P. Fundamentals of Nonlinear Digital Filtering. New York – CRC Press, 1997. 276 p.

Kalluri S., Arce G. R. Adaptive weighted myriad filter algorithms for robust signal processing in -stable noise environments // IEEE Transactions on Signal Processing. 1998. V. 46, No. 2. P. 322 – 334.

Pander T. Impulsive noise filtering in biomedical signals with application of new myriad filter // Biosignal' 2010: Proc. of the Int. Conf. 2010. V. 20. P. 94 – 101.

Gonzalez J. G., Arce G. R. Optimality of the myriad filter in practical impulsive-noise environments // IEEE Transactions on Signal Processing. 2001. V. 49, No. 2. P. 438 – 441. doi: 10.1109/78.902126.

Gonzalez J. G., Arce G. R. Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters // EURASIP J. on Applied Signal Processing. 2002. V. 1, No. 1. P. 4 – 20.

Тулякова Н. О., Трофимчук А. Н., Стрижак А. Е. Алгоритмы мириадной фильтрации // Радиоэлектронные и компьютерные системы. 2014. № 4 (68). С. 76 – 83.

Downloads

How to Cite

Тулякова, Н., & Трофимчук, О. (2020). Adaptive algorithms for real-time filtering of electrocardiogram with multilevel noise estimation. Radiotekhnika, 2(201), 201–214. https://doi.org/10.30837/rt.2020.2.201.20

Issue

No. 201 (2020): Radiotekhnika

Section

Articles

License

Authors who publish with this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).