Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 8, pp 10365–10374 | Cite as

A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

  • C. Venkatesan
  • P. Karthigaikumar
  • R. Varatharajan
Article
  • 147 Downloads

Abstract

ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques.

Keywords

ECG noise removal Adaptive filter Least mean square algorithm Discrete wavelet transform Machine learning KNN classifier 

References

  1. 1.
    Chawla M (2011) PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: a survey and comparison. Appl Soft Comput 11(2):2216–2226.  https://doi.org/10.1016/j.asoc.2010.08.001 CrossRefGoogle Scholar
  2. 2.
    Elgendi M, Jonkman M, Boer FD (2009) R wave detection using Coiflets wavelets. 2009 I.E. 35th annual northeast bioengineering conference 1-2.  https://doi.org/10.1109/nebc.2009.4967756
  3. 3.
    Gupta A, Joshi S (2008) Variable step-size LMS algorithm for fractal signals. IEEE Trans Signal Process 56(4):1411–1420.  https://doi.org/10.1109/tsp.2007.909374 MathSciNetCrossRefGoogle Scholar
  4. 4.
    Jayalalith S, Susan D, Kumari S, Archana B (2014) K-nearest neighbour method of analysing the ECG signal (To Find out the Different Disorders Related to Heart). J Appl Sci 14(14):1628–1632.  https://doi.org/10.3923/jas.2014.1628.1632 CrossRefGoogle Scholar
  5. 5.
    Jubairahmed L, Satheeskumaran S, Venkatesan C (2017) Contourlet transform based adaptive nonlinear diffusion filtering for speckle noise removal in ultrasound images. Cluster Comput 1–10.  https://doi.org/10.1007/s10586-017-1370-x
  6. 6.
    Kohler B, Hennig C, Orglmeister R (2002) The principles of software QRS detection. IEEE Eng Med Biol Mag 21(1):42–57.  https://doi.org/10.1109/51.993193 CrossRefGoogle Scholar
  7. 7.
    Kumar M, Weippert M, Vilbrandt R, Kreuzfeld S, Stoll R (2007) Fuzzy evaluation of heart rate signals for mental stress assessment. IEEE Trans Fuzzy Syst 15(5):791–808.  https://doi.org/10.1109/tfuzz.2006.889825 CrossRefGoogle Scholar
  8. 8.
    Langley P, Bowers E, Wild J, Drinnan M, Allen J, Sims A, Murray A (2003) An algorithm to distinguish ischaemic and non-ischaemic ST changes in the Holter ECG. Comput Cardiol 2003:235–238.  https://doi.org/10.1109/cic.2003.1291135 Google Scholar
  9. 9.
    Nouira I, Abdallah A, Kouaja I, Bedoui M (2012) Comparative study of QRS complex detection in ECG. Proc World Acad Sci Eng Technol 71:1447–1451Google Scholar
  10. 10.
    Osowski S, Hoai L, Markiewicz T (2004) Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans Biomed Eng 51(4):582–589.  https://doi.org/10.1109/tbme.2004.824138 CrossRefGoogle Scholar
  11. 11.
    Parhi K, Messerschmitt D (1989) Pipeline interleaving and parallelism in recursive digital filters. I. Pipelining using scattered look-ahead and decomposition. IEEE Trans Acoust Speech Signal Process 37(7):1099–1117.  https://doi.org/10.1109/29.32286 CrossRefzbMATHGoogle Scholar
  12. 12.
    Petković D, Ćojbašić Ž, Lukić S (2013) Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system. Expert Syst Appl 40(11):4490–4495.  https://doi.org/10.1016/j.eswa.2013.01.055 CrossRefGoogle Scholar
  13. 13.
    Rahman MZ, Shaik RA, Reddy DV (2012) Efficient and simplified adaptive noise cancellers for ECG sensor based remote health monitoring. IEEE Sensors J 12(3):566–573.  https://doi.org/10.1109/jsen.2011.2111453 CrossRefGoogle Scholar
  14. 14.
    Rooijakkers MJ, Rabotti C, Oei SG, Mischi M (2012) Low-complexity R-peak detection for ambulatory fetal monitoring. Physiol Meas 33(7):1135–1150.  https://doi.org/10.1088/0967-3334/33/7/1135 CrossRefGoogle Scholar
  15. 15.
    Satheeskumaran S, Sabrigiriraj M (2014) A new LMS based noise removal and DWT based R-peak detection in ECG signal for biotelemetry applications. Natl Acad Sci Lett 37(4):341–349CrossRefGoogle Scholar
  16. 16.
    Satheeskumaran S, Sabrigiriraj M (2016) VLSI implementation of a new LMS based algorithm for noise removal in ECG signal. Int J Electron 103:975–984CrossRefGoogle Scholar
  17. 17.
    Stolojescu C, Railean I, Moga S, Isar A (2010) Comparison of wavelet families with application to WiMAX traffic forecasting. 2010 12th international conference on optimization of electrical and electronic equipment 932-937.  https://doi.org/10.1109/optim.2010.5510403
  18. 18.
    Szilagyi S, Szilagyi L (2003) Wavelet transform and neural-network-based adaptive filtering for QRS detection. Proceedings of the 22nd annual international conference of the IEEE engineering in medicine and biology society (Cat. No.00CH37143) 2:1267-1270.  https://doi.org/10.1109/iembs.2000.897966
  19. 19.
    Ting L, Woods R, Cowan C (2005) Virtex FPGA implementation of a pipelined adaptive LMS predictor for electronic support measures receivers. IEEE Trans Very Large Scale Integr (VLSI) Syst 13(1):86–95.  https://doi.org/10.1109/tvlsi.2004.840403 CrossRefGoogle Scholar
  20. 20.
    Tsipouras M, Fotiadis D, Sideris D (2005) An arrhythmia classification system based on the RR-interval signal. Artif Intell Med 33(3):237–250.  https://doi.org/10.1016/j.artmed.2004.03.007 CrossRefGoogle Scholar
  21. 21.
    Venkatesan C, Karthigaikumar P, Varatharajan R (2018) FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal. Cluster Comput 1-9.  https://doi.org/10.1007/s10586-017-1602-0
  22. 22.
    Wiggins M, Saad A, Litt B, Vachtsevanos G (2008) Evolving a Bayesian classifier for ECG-based age classification in medical applications. Appl Soft Comput 8(1):599–608.  https://doi.org/10.1016/j.asoc.2007.03.009 CrossRefGoogle Scholar
  23. 23.
    Zhai J, Barreto A (2006) Stress detection in computer users based on digital signal processing of noninvasive physiological variables. 2006 international conference of the IEEE engineering in medicine and biology society 1355-1358.  https://doi.org/10.1109/iembs.2006.4397662
  24. 24.
    Zhang J, Zhao H (2010) A novel adaptive bilinear filter based on pipelined architecture. Digit Signal Process 20(1):23–38.  https://doi.org/10.1016/j.dsp.2009.06.006 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • C. Venkatesan
    • 1
  • P. Karthigaikumar
    • 2
  • R. Varatharajan
    • 3
  1. 1.Faculty of Information and Communication EngineeringAnna UniversityChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringKarpagam College of EngineeringCoimbatoreIndia
  3. 3.Department of Electronics and Communication EngineeringSri Ramanujar Engineering CollegeChennaiIndia

Personalised recommendations