Advertisement

R-peak detection based chaos analysis of ECG signal

  • Varun GuptaEmail author
  • Monika Mittal
  • Vikas Mittal
Article
  • 25 Downloads

Abstract

Electrocardiography (ECG) is a non-invasive test that is used for recording contraction and relaxation activities of the heart by using an electrocardiogram. Early detection of abnormalities of the heart through ECG is essential for reducing the prevalence of casualties due to cardiac arrests worldwide. In this study, physioNet ECG records have been considered for analysis. During recording, ECG signal is also affected by various noises, where analog filters fail due to the effect of temperature and drift, and digital filters fail due to inappropriate selection of passband and gain parameters. For adequate and frequent usage in the medical field, it demands correct and precise R-peak (QRS-complex) detection; which requires an appropriate combination of pre-processing, feature extraction and detection techniques. Therefore, independent component analysis (ICA) is used in the pre-processing stage due to nonlinear nature of the ECG signals and chaos analysis is applied for feature extraction for different ECG databases. The ICA method separates an individual signal from mixed signals by assuming that the original underlying source signals are mutually independently distributed. Chaos analysis examines the irregular attitude of the system and fits it into deterministic equations of motion. Chaos analysis is implemented by plotting different attractors against various time delay dimensions. R-peak detection is well known to be useful in diagnosing cardiac diseases. The R-peaks are detected using principal component analysis (PCA) which outperforms the existing state-of-the-art techniques.

Keywords

Electrocardiography (ECG) Independent component analysis (ICA) Chaos analysis R-peak detection Cardiac arrests 

Notes

References

  1. 1.
    Luz, E. J. S., Schwartz, W. R., Chávez, G. C., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine,127, 144–164.CrossRefGoogle Scholar
  2. 2.
    Vandeput, S.(2010). Heart rate variability: Linear and nonlinear analysis with applications in human physiology. Doctor in Engineering Sciences, Katholieke Universiteit, Leuven, Belgium.Google Scholar
  3. 3.
    Rajankar, S. O., & Talbar, S. N. (2017). Adaptive vector K-tree partitioning an entropy coder: Application to ECG compression. International Journal of Telemedicine and Clinical Practices Inderscience,2(3), 215–224.CrossRefGoogle Scholar
  4. 4.
    Gupta, V., & Mittal, M. (2018). KNN and PCA classifier with autoregressive modelling during different ECG signal interpretation. Procedia Computer Science,125, 18–24.CrossRefGoogle Scholar
  5. 5.
    Rajankar, S. O., & Talbar, S. N. (2019). An electrocardiogram signal compression techniques: a comprehensive review. Analog Integrated Circuits and Signal Processing,98(1), 59–74.CrossRefGoogle Scholar
  6. 6.
    Gupta, V., & Mittal, M. (2018). Electrocardiogram signals interpretation using Chaos Theory. Journal of Advanced Research in Dynamical and Control Systems,9, 2392–2397.Google Scholar
  7. 7.
    Gorgels, A. P. M., Willerson, J. T., Wellens, H. J. J., Cohn, J. N., & Holmes, D. R. (2007). Cardiovascular medicine. London: Springer.Google Scholar
  8. 8.
    Kaya, Y., & Pehlivan, H. (2015). Comparison of classification algorithms in classification of ECG beats by time series. In 2015 IEEE conference on signal processing and communications applications (SIU) (pp. 407–410).Google Scholar
  9. 9.
    Perlman, O., Katz, A., Weissman, N., Amit, G., & Zigel, Y. (2014). Atrial electrical activity detection using linear combination of 12-lead ECG signals. IEEE Transactions on Biomedical Engineering,61, 1034–1043.CrossRefGoogle Scholar
  10. 10.
    Javadi, M., Arani, S. A. A. A. A., Sajedin, A., & Ebrahimpour, R. (2013). Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomedical Signal Processing and Control,8, 289–296.CrossRefGoogle Scholar
  11. 11.
    Liu, X., Yang, J., Zhu, X., Zhou, S., Wang, H., & Zhang, H. (2014). A novel R-peak detection method combining energy and wavelet transform in electrocardiogram signal. Biomedical Engineering: Applications, Basis and Communications,26, 1–9.Google Scholar
  12. 12.
    Kaya, Y., Pehlivan, H., & Tenekeci, M. E. (2017). Effective ECG beat classification using higher order statistic features and genetic feature selection. The Journal of Biological Research,28, 7594–7603.Google Scholar
  13. 13.
    Klingspor, M. (2015). Hilbert transform: Mathematical theory and applications to signal processing. Linkoping: Linkopings University.Google Scholar
  14. 14.
    Yeh, Y. C., Wang, W. J., & Chiou, C. W. (2009). Cardiac arrhythmia diagnosis method using LDA on ECG signals. Measurement,42, 778–789.CrossRefGoogle Scholar
  15. 15.
    Andreao, R. V., Dorizzi, B., & Boudy, J. (2006). ECG signal analysis through hidden Markov models. IEEE Transactions on Biomedical Engineering,53, 1541–1549.CrossRefGoogle Scholar
  16. 16.
    Malek, A., Katariya, S., Chow, Y., & Ghavamzadeh, M. (2017). Sequential multiple hypothesis testing with type i error control. In Proceedings of the 20th international conference on artificial intelligence and statistics, USA (pp. 1468–1476).Google Scholar
  17. 17.
    Li, Y., Yan, H., Hong, F., & Song, J. (2012). A new approach of QRS complex detection based on matched filtering and triangle character analysis. Australasian Physical & Engineering Sciences in Medicine,35, 341–356.CrossRefGoogle Scholar
  18. 18.
    Zidelmal, Z., Amirou, A., Adnane, M., & Belouchrani, A. (2012). QRS detection based on wavelet coefficients. Computer Methods and Programs in Biomedicine,107, 490–496.CrossRefGoogle Scholar
  19. 19.
    He, R., Wang, K., Li, Q., Yuan, Y., Zhao, N., Liu, Y., et al. (2017). A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization. EURASIP Journal on Advances in Signal Processing,82, 1–14.Google Scholar
  20. 20.
    Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences,405, 81–90.CrossRefGoogle Scholar
  21. 21.
    Rahhal, M. M. A., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., & Yager, R. R. (2016). Deep learning approach for active classification of electrocardiogram signals. Information Sciences,345, 340–354.CrossRefGoogle Scholar
  22. 22.
    Ahmadian, A., Karimifard, S., Sadoughi, H., & Abdoli, M. (2007). An efficient piecewise modeling of ECG signals based on hermitian basis functions. In IEEE 2007 engineering in medicine and biology society, Lyon, France (pp. 3180–3183).Google Scholar
  23. 23.
    Kaya, Y., & Pehlivan, H. (2015). Classification of premature ventricular contraction in ECG. International Journal of Advanced Computer Science and Applications,6, 34–40.CrossRefGoogle Scholar
  24. 24.
    Rao, K. D. (1997). DWT Based Detection of R-peaks and Data Compression of ECG Signals. IETE Journal of Research,43, 345–349.CrossRefGoogle Scholar
  25. 25.
    Kaur, H., & Rajni, R. (2017). Electrocardiogram signal analysis for R-peak detection and denoising with hybrid linearization and principal component analysis. Turkish Journal of Electrical Engineering & Computer Sciences,25, 2163–2175.CrossRefGoogle Scholar
  26. 26.
    Sahambi, J., Tandon, S., & Bhatt, R. (1997). Using wavelet transforms for ECG characterization. IEEE Engineering in Medicine and Biology Magazine,16, 77–83.CrossRefGoogle Scholar
  27. 27.
    Hongyan, X., & Minsong, H. (2008). A new qrs detection algorithm based on empirical mode decomposition. In IEEE 2008 2nd international conference ICBBE, Shanghai, China (pp. 693–696).Google Scholar
  28. 28.
    Aurobinda, A., Mohanty, B.P., & Mohanty, M.N. (2016). R-peak Detection of ECG using Adaptive Thresholding. In IEEE 2016 international conference on communication and signal processing, Madras, India (pp. 0284–0287).Google Scholar
  29. 29.
    Jaafar, H., Ramli, N.H., & Nasir, A.S.A. (2018). An improvement to The k-nearest neighbor classifier for ECG database. In IOP conference on series: materials science and engineering, Penang, Malaysia (pp. 1–10).Google Scholar
  30. 30.
    Pahim, O., & Sornmo, L. (1984). Software QRS detection in ambulatory monitoring-a review. Medical and Biological Engineering and Computing,22, 289–297.CrossRefGoogle Scholar
  31. 31.
    Saini, I., Singh, D., & Khosla, A. (2013). QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases. Journal of Advanced Research,4, 331–344.CrossRefGoogle Scholar
  32. 32.
    Petricek, M. (2010). Components in data analysis. In WDS’10 proceedings of contributed papers, 0104 June 2010; Prague, Bohemia: Matfyzpress (pp. 82–87).Google Scholar
  33. 33.
    Kuzilek, J., Kremen, V., & SoucekF, Lhotska L. (2014). Independent component analysis and decision trees for ECG holter recording de-noising. Plos One,9, 1–9.CrossRefGoogle Scholar
  34. 34.
    Sayadi, O., & Shamsollahi, M. B. (2007). Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction. Journal on Advances in Signal Processing,14, 1–11.zbMATHGoogle Scholar
  35. 35.
    Rekik, S., & Ellouze, N. (2017). Enhanced and optimal algorithm for QRS detection. IRBM,38(1), 56–61.CrossRefGoogle Scholar
  36. 36.
    Gupta, V., & Mittal, M. (2019). QRS complex detection Using STFT, chaos analysis, and PCA in standard and real-time ECG databases. Inst: Journal of The Institution of Engineers (India): Series B.  https://doi.org/10.1007/s40031-019-00398-9.CrossRefGoogle Scholar
  37. 37.
    Casdagli, M. (1992). Chaos and deterministic versus stochastic nonlinear modeling. Journal of the Royal Statistical Society: Series B (Methodological),54, 303–328.MathSciNetGoogle Scholar
  38. 38.
    Nguomkam Negou, A., & Kengne, J. (2019). A minimal three-term chaotic flow with coexisting routes to chaos, multiple solutions, and its analog circuit realization. Analog Integrated Circuits and Signal Processing.  https://doi.org/10.1007/s10470-019-01436-8.CrossRefGoogle Scholar
  39. 39.
    Nguomkam Negou, A., & Kengne, J. (2018). Dynamic analysis of a unique jerk system with a smoothly adjustable symmetry and nonlinearity: Reversals of period doubling, offset boosting and coexisting bifurcations. International Journal of Electronics and Communications,90, 1–19.CrossRefGoogle Scholar
  40. 40.
    Xingyuan, W., & Juan, M. (2009). Wavelet-based hybrid ECG compression technique. Analog Integrated Circuits and Signal Processing,59(3), 301–308.CrossRefGoogle Scholar
  41. 41.
    Mehta, S. S., Shete, D. A., Lingayat, N. S., & Chouhan, V. S. (2010). K-means algorithm for the detection and delineation of QRS-complexes in electrocardiogram. IRBM,31(1), 48–54.CrossRefGoogle Scholar
  42. 42.
    Kutlu, Y., & Kuntalp, D. (2012). Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Computer Methods and Programs in Biomedicine,105, 257–267.CrossRefGoogle Scholar
  43. 43.
    Dokur, Z., & Ölmez, T. (2001). ECG beat classification by a novel hybrid neural network. Computer methods and programs in Biomedicine,66, 167–181.CrossRefGoogle Scholar
  44. 44.
    Stone, J. V. (2004). Independent component analysis: A tutorial introduction. Cambridge: Bradford Books, The MIT Press.CrossRefGoogle Scholar
  45. 45.
    Naik, G. R., & Kumar, D. K. (2011). An overview of independent component analysis and its applications. Informatica,35, 63–81.zbMATHGoogle Scholar
  46. 46.
    Lai, Q., Tsafack, N., Kengne, J., & Zhao, X. W. (2018). Coexisting attractors and circuit implementation of a new 4D chaotic system with two equilibria. Chaos, Solitons & Fractals,107, 92–102.MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Lai, Q., & Chen, S. (2016). Coexisting attractors generated from a new 4D smooth chaotic system. International Journal of Control, Automation and Systems,14(4), 1124–1131.CrossRefGoogle Scholar
  48. 48.
    Akgul, A., Calgan, H., Koyuncu, I., Pehlivan, I., & Istanbullu, A. (2015). Chaos-based engineering applications with a 3D chaotic system without equilibrium points. Nonlinear Dynamics,1, 1.  https://doi.org/10.1007/s11071-015-2501-7.CrossRefGoogle Scholar
  49. 49.
    Acharya, R., Kumar, A., Bhat, P. S., Lim, C. M., Lyengar, S. S., Kannathal, N., et al. (2004). Classification of cardiac abnormalities using heart rate signals. Medical and Biological Engineering and Computing,42, 288–293.CrossRefGoogle Scholar
  50. 50.
    Eckman, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhsics Letters,4, 973–977.CrossRefGoogle Scholar
  51. 51.
    Skiadas, C. H., & Skiadas, C. (2016). Handbook of applications of chaos theory (1st ed.). New York: Taylor & Francis, CRC Press.zbMATHGoogle Scholar
  52. 52.
    Abarbanel, H. D. I. (1996). Analysis of Observed Chaotic Data (2nd ed.). New York: Springer.zbMATHCrossRefGoogle Scholar
  53. 53.
    Bradley, E., & Kantz, H. (2015). Nonlinear time-series analysis revisited. Journal of Chaos,25, 09761001–09761010.MathSciNetzbMATHGoogle Scholar
  54. 54.
    Kaplan, D. T., & Glass, L. (1992). Direct test for determinism in a time series. Physical Review Letters,68, 427–430.CrossRefGoogle Scholar
  55. 55.
    Briggs, K. (1987). Simple experiments in chaotic dynamics. American Journal of Physics,55, 1083–1089.CrossRefGoogle Scholar
  56. 56.
    Alickovic, E., & Subasi, A. (2015). Effect of Multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases. Circuits, Systems, and Signal Processing,34, 513–533.CrossRefGoogle Scholar
  57. 57.
    Sheetal, A., Singh, H., & Kaur, A. (2019). QRS detection of ECG signal using hybrid derivative and MaMeMi filter by effectively eliminating the baseline wander. Analog Integrated Circuits and Signal Processing,98(1), 1–9.CrossRefGoogle Scholar
  58. 58.
    Ren, S., Panahi, S., Rajagopal, K., Akgul, A., Pham, V.-T., & Jafari, S. (2018). A new chaotic flow with hidden attractor: The first hyperjerk system with no equilibrium. Zeitschrift für Naturforschung.  https://doi.org/10.1515/zna-2017-0409.CrossRefGoogle Scholar
  59. 59.
    Nguomkam Negou, A., Kengne, J., & Tchiotsop, D. (2018). Periodicity, chaos and multiple coexisting attractors in a generalized Moore-Spiegel system. Chaos, Solitons & Fractals,107, 275–289.MathSciNetzbMATHCrossRefGoogle Scholar
  60. 60.
    Luo, X., & Small, M. (2007). On a dynamical system with multiple chaotic attractors. International Journal of Bifurcation and Chaos,17(9), 3235–3251.MathSciNetzbMATHCrossRefGoogle Scholar
  61. 61.
    Gupta, V., & Mittal, M. (2018). Dimension reduction and classification in ECG signal interpretation using FA and PCA: A comparison. Jangjeon Mathematical Society,21(4), 765–777.MathSciNetzbMATHGoogle Scholar
  62. 62.
    Sahoo, S., Biswal, P., Das, T., & Sabut, S. (2016). De-noising of ECG signal and QRS detection using hilbert transform and adaptive thresholding. Procedia Technology,25, 68–75.CrossRefGoogle Scholar
  63. 63.
    Ghaffari, A., Golbayani, H., & Ghasemi, M. (2008). A new mathematical based QRS detector using continuous wavelet transform. Computers & Electrical Engineering,34, 81–91.zbMATHCrossRefGoogle Scholar
  64. 64.
    Yazdani, S., & Vesin, J. M. (2016). Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology. Digital Signal Processing,56, 100–109.MathSciNetCrossRefGoogle Scholar
  65. 65.
    Manikandan, M. S., & Soman, K. P. (2012). A novel method for detecting R-peaks in electrocardiogram signal. Biomedical Signal Processing and Control,7, 118–128.CrossRefGoogle Scholar
  66. 66.
    Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering,1, 21–28.Google Scholar
  67. 67.
    Lee, J., Jeong, K., Yoon, J., Lee, M. (1996). A simple real-time QRS detection algorithm, In Proceedings of the 18th IEEE annual international conference engineering in medicine and biology society, Netherlands (pp. 1396–1398).Google Scholar
  68. 68.
    Poli, R., Cagnoni, S., & Valli, G. (1995). Genetic design of optimum linear and nonlinear QRS detectors. IEEE Transactions on Biomedical Engineering,42(11), 1137–1141.CrossRefGoogle Scholar
  69. 69.
    Afonso, V., Tompkins, W. J., Nguyen, T., & Luo, S. (1999). ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering,46(2), 192–202.CrossRefGoogle Scholar
  70. 70.
    Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering,3, 230–236.CrossRefGoogle Scholar
  71. 71.
    Martis, R. J., Acharya, U. R., Lim, C. M., & Suri, J. S. (2013). Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowledge-Based Systems,45, 76–82.CrossRefGoogle Scholar
  72. 72.
    Martis, R. J., Acharya, U. R., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2012). Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Systems with Applications,39, 11792–11800.CrossRefGoogle Scholar
  73. 73.
    Chiu, C. C., Lin, T. H., & Liau, B. Y. (2005). Using correlation coefficient in ECG waveform for arrhythmia detection. Biomedical Engineering: Applications, Basis and Communications,17, 37–42.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.KIET Group of InstitutionsMuradnagar, GhaziabadIndia
  2. 2.Department of Electrical EngineeringNITKurukshetraIndia
  3. 3.Department of Electronics and Communication EngineeringNITKurukshetraIndia

Personalised recommendations