Abstract
The electrocardiogram is a graphical record of the biological signal that is thought to be susceptible to electrical activity of the heart and utilized in order to clinical diagnosis. Electrocardiogram signal is very responsive in nature, and even if there is small noise mixed with the original signal, assorted characteristics of signal change. ECG signal voltage level is as low as 0.5 to 5 mV and is sensitive to artifacts larger than this. Human electrocardiogram signal range frequency ingredients from 0.05 Hz to 100 Hz and are related to noise, muscle movements, network current, and ambient electromagnetic interference. Electrocardiogram is a very significant sign detects abnormal heart rhythms and examines cause of chest pain and widely utilized in cardiology. Most digital signals are infinitely large or too large to be manipulated as a whole. Because statistical calculations require that all points be present for analysis, it is difficult to statistically analyze sufficiently large signals. To avoid these problems, engineers characteristically analyze small subsets of the aggregate data with an operation named windowing. Fuzzy logic is a mathematical logic that attempts to solve problems with a clear, uncertain data spectrum that makes it possible to obtain a series of correct results. This manuscript suggests denoising method Gaussian Weighted Moving Windowing for denoising Electrocardiogram signals to remove random noise. This study is interpreted with the actual data set and confirmed according to Peak signal to noise ratio.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nayak, S., Soni, M.K., Bansal, D.: Filtering techniques for ECG signal processing. Int. J. Res. Eng. Appl. Sci., IJREAS 2(2), 671 (2012). ISSN 2249-3905
Correia, S., Miranda, J., Silva, L., Barreto, A.: LABVIEW and MATLAB for ECG acquisition, filtering and processing. In: 3rd International Conference on Integrity, Reliability and Failure, Porto, Portugal, 20–24 July (2009)
Pedrycz, W.: Fuzzy Control and Fuzzy Systems, 2nd edn. Research Studies Press Ltd., Taunton (1993)
Anderson, B.D., Moore, J.B.: Optimal Filtering. Dover Publications Inc., Mineola (2005)
Subhadeep, C.: Advantages of Blackman window over hamming window method for designing FIR filter. Int. J. Comput. Sci. Eng. Technol. 4(8), 1181–1189 (2013)
Nagarajan, T., Prasad, V.K., Murthy, H.A.: Minimum phase signal derived from root cepstrum. IEE Electron. Lett. 39(12), 941–942 (2003)
Badiru, A.B., Cheung, J.Y.: Fuzzy Engineering Expert Systems with Neural Network Applications. Wiley, New York (2002)
Sandya, H.B., Hemanth Kumar, P., Himanshi, B., Susham, K.R.: Fuzzy rule based feature extraction and classification of time series signal. Int. J. Soft Comput. Eng. (IJSCE) 3, 42–47 (2013)
Sadıkoğlu, F., Kavalcıoğlu, C., Dağman, B.: Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease. Procedia Comput. Sci. (2017). https://doi.org/10.1016/j.procs.2017.11.259
Kavalcıoğlu, C., Dağman, B.: Filtering maternal and fetal electrocardiogram (ECG) signals using Savitzky-Golay filter and adaptive least mean square (LMS) cancellation technique. Bull. Transylvania Univ. Bras.: Ser. III: Math., Inform., Phys. 9(58)(2), 109–124 (2016)
Sadıkoğlu, F., Kavalcıoğlu, C.: Filtering continuous glucose monitoring signal using Savitzky-Golay filter and simple multivariate thresholding. Procedia Comput. Sci. (2016). https://doi.org/10.1016/j.procs.2016.09.410
Gomes, P.R., Soares, F.O., Correia, J.H.: ECG self diagnosis system at P- R interval. In: Proceedings of VIPIMAGE, pp. 287–290 (2007)
Pinheiro, E., Postolache, O., Pereira, J.M.D.: A practical approach concerning heart rate variability measurement and arrhythmia detection based on virtual instrumentation, pp. 112–115 (2007)
Sornmo, L., Laguna, P.: Electrocardiogram signal processing. In: Wiley Encyclopedia of Biomedical Engineering (2006)
Yatindra, K., Malik, G.K.: Performance analysis of different filters for power line interface reduction in ECG signal. Int. J. Comput. Appl. 3(7), 1–6 (2010)
Joshi, P.J., Patkar, V.P., Pawar, A.B., Patil, P.B., Bagal, U.R., Mokal, B.D.: ECG denoising using MATLAB. Int. J. Sci. Eng. Res. 4, 1401–1405 (2013)
Birle, A., Malviya, S., Mittal, D.: Noise removal in ECG signal using Savitzky - Golay filter. Int. J. Adv. Res. Electron. Commun. Eng. (IJARECE) 4, 1331–1333 (2015)
Meireles, A.J.M.: ECG denoising based on adaptive signal processing technique. Master thesis, ISEP Instituto Superior de Engenharia do Porto (2011)
AlMahamdy, M., Riley, H.B.: Performance study of different denoising methods for ECG signals. In: The 4th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2014) (2014)
Chandrika, B., Yadav, O.P., Chandra, V.K.: A survey of noise removal techniques for ECG signals. Int. J. Adv. Res. Comput. Commun. Eng. 2, 1354–1357 (2013)
Islam, M.K., Haque, A.N.M.M., Tangim, G., Ahammad, T., Khondokar, H.: Study and analysis of ECG signal using MATLAB & LABVIEW as effective tools. Int. J. Comput. Electr. Eng. 4, 404–408 (2012)
Kavitha, R., Christopher, T.: A study on ECG signal classification techniques. Int. J. Comput. Appl. 86, 9–14 (2014)
Kumar, N., Ahmad, I., Rai, P.: Signal processing of ECG using Matlab. Int. J. Sci. Res. Publ. 2, 1–6 (2012)
Nayak, S., Soni, K.M., Bansal, D.: Filtering techniques for ECG signal processing. IJREAS 2, 2249–3905 (2012)
PubMed: The U. S. National Library of Medicine and the National Institutes of Health, A service of the U.S. (2008). http://www.pubmed.gov/
Kasar, S., Mishra, A., Joshi, M.: Performance of digital filters for noise removal from ECG signals in time domain. Int. J. Innov. Res. Electr., Electron., Instrum. Control. Eng. 2, 1352–1355 (2014)
Jantzen, J.: Tutorial on fuzzy logic. Technical University of Denmark (2008)
Güler, I., Ubeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods 148, 113–121 (2005)
Matsuyama, A., Jonkman, M., de Boer, F.: Improved ECG signal analysis using wavelet and feature extraction. Methods Inf. Med. 46, 227–230 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kavalcıoğlu, C., Bilgehan, B. (2019). A Fuzzy Based Gaussian Weighted Moving Windowing for Denoising Electrocardiogram (ECG) Signals. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-04164-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04163-2
Online ISBN: 978-3-030-04164-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)