http://iet.metastore.ingenta.com
1887

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Science, Measurement & Technology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Time–frequency representation (TFR) is useful for non-stationary signal analysis as it provides information about the time-varying frequency components. This study proposes a novel TFR based on the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM–HT). In the proposed method, first the authors decompose non-stationary signals using the IEVDHM with suitably defined criterion for eigenvalue selection, requirement of number of iterations, and new component merging criteria. Furthermore, the HT is applied on extracted components in order to obtain the TFR of non-stationary signals. The performance of proposed TFR has been evaluated on synthetic signals in clean and white noise environment with different signal-to-noise ratios. The proposed method gives good performance in terms of Rényi entropy measure in comparison with other existing methods. Application of the proposed TFR is also shown for the classification of epileptic seizure electroencephalogram (EEG) signals. The least-square support vector machine (LS-SVM) with radial basis function kernel is used for classification of seizure and seizure-free EEG signals obtained from the publicly available database by the University of Bonn, Germany. The proposed method has achieved classification accuracy 100% for the studied EEG database.

References

    1. 1)
      • 1. Boashash, B.: ‘Time-frequency signal analysis and processing: a comprehensive reference’ (Elsevier, 2003).
    2. 2)
      • 2. Stanković, L., Dakovic, M., Thayaparan, T.: ‘Time-frequency signal analysis with applications’ (Artech House, 2013).
    3. 3)
      • 3. Sejdić, E., Djurovic, I., Jiang, J.: ‘Time–frequency feature representation using energy concentration: an overview of recent advances’, Digit. Signal Process., 2009, 19, pp. 153183.
    4. 4)
      • 4. Qian, S., Chen, D.: ‘Joint time–frequency analysis’, IEEE Signal Process. Mag., 1999, 16, pp. 5267.
    5. 5)
      • 5. Rakovic, P., Sejdic, E., Stankovic, L.J., et al: ‘Time–frequency signal processing approaches with applications to heart sound analysis’, Comput. Cardiol., 2006, 33, pp. 197200.
    6. 6)
      • 6. Boashash, B., Azemi, G., Khan, N.A.: ‘Principles of time–frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection’, Pattern Recognit., 2015, 48, pp. 616627.
    7. 7)
      • 7. Yan, W., Zhang, Z., Qu, J., et al: ‘Time–frequency distribution decomposition with applications to recognize the looseness state of the viscoelastic sandwich structure’, Meas. Sci. Technol., 2016, 27, p. 075001.
    8. 8)
      • 8. Antoniadou, I., Manson, G., Staszewski, W.J., et al: ‘A time–frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions’, Mech. Syst. Signal Process., 2015, 64, pp. 188216.
    9. 9)
      • 9. Chakraborty, A., Okaya, D.: ‘Frequency-time decomposition of seismic data using wavelet-based methods’, Geophysics, 1995, 60, pp. 19061916.
    10. 10)
      • 10. Shao, Y., Chang, C.H.: ‘A generalized time–frequency subtraction method for robust speech enhancement based on wavelet filter banks modeling of human auditory system’, IEEE Trans. Syst. Man Cybern. B (Cybern.), 2007, 37, pp. 877889.
    11. 11)
      • 11. Boashash, B., Khan, N.A., Ben-Jabeur, T.: ‘Time–frequency features for pattern recognition using high-resolution TFDs: a tutorial review’, Digit. Signal Process., 2015, 40, pp. 130.
    12. 12)
      • 12. Sun, K., Jin, T., Yang, D.: ‘An improved time–frequency analysis method in interference detection for GNSS receivers’, Sensors, 2015, 15, pp. 94049426.
    13. 13)
      • 13. Meyer, Y.: ‘Wavelets and operators’ (Cambridge university press, 1995), vol. 1.
    14. 14)
      • 14. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’. Proc. Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1971, pp. 903995.
    15. 15)
      • 15. Daubechies, I., Lu, J., Wu, H.T.: ‘Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool’, Appl. Comput. Harmon. Anal., 2011, 30, pp. 243261.
    16. 16)
      • 16. Kadambe, S., Boudreaux-Bartels, G.F.: ‘A comparison of the existence of ‘cross terms’ in the Wigner distribution and the squared magnitude of the wavelet transform and the short-time Fourier transform’, IEEE Trans. Signal Process., 1992, 40, pp. 24982517.
    17. 17)
      • 17. Chen, V.C., Ling, H.: ‘Time-frequency transforms for radar imaging and signal analysis’ (Artech House, 2001).
    18. 18)
      • 18. Qaisar, S.M., Fesquet, L., Renaudin, M.: ‘An adaptive resolution computationally efficient short-time Fourier transform’, J. Electr. Comput. Eng., 2008, 2008, pp. 10.110.5.
    19. 19)
      • 19. Jiang, Y., He, Y.: ‘Frequency estimation of electric signals based on the adaptive short-time Fourier transform’, Int. J. Electron., 2009, 96, pp. 267279.
    20. 20)
      • 20. Kim, B., Kong, S.H., Kim, S.: ‘Low computational enhancement of STFT-based parameter estimation’, IEEE J. Sel. Top. Signal Process., 2015, 9, pp. 16101619.
    21. 21)
      • 21. Yu, S., You, X., Ou, W., et al: ‘STFT like time–frequency representations of nonstationary signal with arbitrary sampling schemes’, Neurocomputing, 2016, 204, pp. 211221.
    22. 22)
      • 22. Cui, J., Wong, W.: ‘The adaptive Chirplet transform and visual evoked potentials’, IEEE Trans. Biomed. Eng., 2006, 53, pp. 13781384.
    23. 23)
      • 23. Yang, Y., Peng, Z.K., Meng, G., et al: ‘Spline-kernelled Chirplet transform for the analysis of signals with time-varying frequency and its application’, IEEE Trans. Ind. Electron., 2012, 59, pp. 16121621.
    24. 24)
      • 24. Yang, Y., Peng, Z.K., Meng, G., et al: ‘Characterize highly oscillating frequency modulation using generalized Warblet transform’, Mech. Syst. Signal Process., 2012, 26, pp. 128140.
    25. 25)
      • 25. Szu, H., Sheng, Y., Chen, J., et al: ‘Wavelet transform as a bank of the matched filters’, Appl. Opt., 1992, 31, pp. 32673277.
    26. 26)
      • 26. Graps, A.: ‘An introduction to wavelets’, IEEE Comput. Sci. Eng., 1995, 2, pp. 5061.
    27. 27)
      • 27. Khan, N.A., Taj, I.A., Jaffri, M.N., et al: ‘Cross-term elimination in Wigner distribution based on 2D signal processing techniques’, Signal Process., 2011, 91, pp. 590599.
    28. 28)
      • 28. Pachori, R.B., Sircar, P.: ‘A new technique to reduce cross terms in the Wigner distribution’, Digit. Signal Process., 2007, 17, pp. 466474.
    29. 29)
      • 29. Pachori, R.B., Nishad, A.: ‘Cross-terms reduction in the Wigner–Ville distribution using tunable-Q wavelet transform’, Signal Process., 2016, 120, pp. 288304.
    30. 30)
      • 30. Ren, H., Ren, A., Li, Z.: ‘A new strategy for the suppression of crossterms in pseudo Wigner–Ville distribution’, Signal Image Video Process., 2016, 10, pp. 139144.
    31. 31)
      • 31. Pachori, R.B., Sircar, P.: ‘Time–frequency analysis using time-order representation and Wigner distribution’. Proc. TENCON 2008 – IEEE Region 10 Conf., Hyderabad, November 2008, pp. 16.
    32. 32)
      • 32. Dragomiretskiy, K., Zosso, D.: ‘Variational mode decomposition’, IEEE Trans. Signal Process., 2014, 62, pp. 531544.
    33. 33)
      • 33. Sharma, R.R., Pachori, R.B.: ‘A new method for non-stationary signal analysis using eigenvalue decomposition of the Hankel matrix and Hilbert transform’. Proc. Fourth Int. Conf. Signal Processing and Integrated Networks, Noida, India, February 2017, pp. 484488.
    34. 34)
      • 34. Jain, P., Pachori, R.B.: ‘An iterative approach for decomposition of multi-component non-stationary signals based on eigenvalue decomposition of the Hankel matrix’, J. Franklin Inst., 2015, 352, pp. 40174044.
    35. 35)
      • 35. Stanković, L.: ‘A measure of some time–frequency distributions concentration’, Signal Process., 2001, 81.3, pp. 621631.
    36. 36)
      • 36. Pachori, R.B., Sircar, P.: ‘EEG signal analysis using FB expansion and second-order linear TVAR process’, Signal Process., 2008, 88.2, pp. 415420.
    37. 37)
      • 37. Pachori, R.B.: ‘Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition’, Res. Lett. Signal Process., 2008, 14, pp. 14.114.5.
    38. 38)
      • 38. Sharma, R., Pachori, R.B.: ‘Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions’, Expert Syst. Appl., 2015, 42.3, pp. 11061117.
    39. 39)
      • 39. Alam, S.S., Syed, T.: ‘EEG signal discrimination using non-linear dynamics in the EMD domain’, Int. J. Comput. Electr. Eng., 2012, 4.3, p. 326.
    40. 40)
      • 40. Martis, R.J., Acharya, U.R., Tan, J.H., et al: ‘Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals’, Int. J. Neural Syst., 2012, 22.06, p. 1250027.
    41. 41)
      • 41. Bajaj, V., Pachori, R.B.: ‘Classification of seizure and nonseizure EEG signals using empirical mode decomposition’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (6), pp. 11351142.
    42. 42)
      • 42. Pachori, R.B., Bajaj, V.: ‘Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition’, Comput. Methods Programs Biomed., 2012, 104, (3), pp. 373381.
    43. 43)
      • 43. Ur Rehman, N., Xia, Y., Mandic, D.P.: ‘Application of multivariate empirical mode decomposition for seizure detection in EEG signals’. Proc. 2010 Annual Int. Conf. Engineering in Medicine and Biology Society (EMBC), August 2010, pp. 16501653.
    44. 44)
      • 44. Zahra, A., Kanwal, N., Ur Rehman, N., et al: ‘Seizure detection from EEG signals using multivariate empirical mode decomposition’, Comput. Biol. Med., 2017, 88, pp. 132141.
    45. 45)
      • 45. Ghosh, D., Dutta, S., Chakraborty, S.: ‘Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status’, Chaos Solitons Fractals, 2014, 67, pp. 110.
    46. 46)
      • 46. Uthayakumar, R., Easwaramoorthy, D.: ‘Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform’, Fractals, 2013, 21.02, p. 1350011.
    47. 47)
      • 47. Easwaramoorthy, D., Uthayakumar, R.: ‘Improved generalized fractal dimensions in the discrimination between healthy and epileptic EEG signals’, J. Comput. Sci., 2011, 2.1, pp. 3138.
    48. 48)
      • 48. Uthayakumar, R., Easwaramoorthy, D.: ‘Multifractal-wavelet based denoising in the classification of healthy and epileptic EEG signals’, Fluct. Noise Lett., 2012, 11.04, p. 1250034.
    49. 49)
      • 49. Ocak, H.: ‘Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy’, Expert Syst. Appl., 2009, 36, (2), pp. 20272036.
    50. 50)
      • 50. Gajic, D., Djurovic, Z., Gligorijevic, J., et al: ‘Detection of epileptiform activity in EEG signals based on time–frequency and non-linear analysis’, Front. Comput. Neurosci., 2015, 9, p. 38.
    51. 51)
      • 51. Bhati, D., Sharma, M., Pachori, R.B., et al: ‘Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification’, Digit. Signal Process., 2017, 62, pp. 259273.
    52. 52)
      • 52. Oliver, F., Acharya, U.R., Adeli, H., et al: ‘Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis’, Seizure, 2015, 26, pp. 5664.
    53. 53)
      • 53. Chen, D., Wan, S., Xiang, J., et al: ‘A high-performance seizure detection algorithm based on discrete wavelet transform (DWT) and EEG’, PloS One, 2017, 12.3, p. e0173138.
    54. 54)
      • 54. Li, Y., Wang, X., Luo, L., et al: ‘Epileptic seizure classification of EEGs using time–frequency analysis based multiscale radial basis functions’, IEEE J. Biomed. Health Inf., 2017, DOI: 0.1109/JBHI.2017.2654479.
    55. 55)
      • 55. Azar, A.T., El-Said, S.A.: ‘Performance analysis of support vector machines classifiers in breast cancer mammography recognition’, Neural Comput. Appl., 2014, 24, (5), pp. 11631177.
    56. 56)
      • 56. Boashash, B., Azemi, G., O'Toole, J.M.: ‘Time–frequency processing of nonstationary signals: advanced TFD design to aid diagnosis with highlights from medical applications’, IEEE Signal Process. Mag., 2013, 30.6, pp. 108119.
    57. 57)
      • 57. Stankovic, L.: ‘On the realization of the polynomial Wigner–Ville distribution for multicomponent signals’, IEEE Signal Process. Lett., 1998, 5, pp. 157159.
    58. 58)
      • 58. Andrzejak, R.G., Lehnertz, K., Mormann, F., et al: ‘Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state’, Phys. Rev. E, 2001, 64, (6), pp. 061907.
    59. 59)
      • 59. Bhattacharya, A., Pachori, R.B.: ‘A multivariate approach for patient specific EEG seizure detection using empirical wavelet transform’, IEEE Trans. Biomed. Eng., 2017, 64, pp. 20032015.
    60. 60)
      • 60. Chaudhuri, B.B., Sarkar, N.: ‘Texture segmentation using fractal dimension’, IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, (1), pp. 7277.
    61. 61)
      • 61. Pertuz, S., Puig, D., Garcia, M.A.: ‘Analysis of focus measure operators for shape-from-focus’, Pattern Recognit., 2013, 46, (5), pp. 14151432.
    62. 62)
      • 62. Dunham, M.H.: ‘Data mining: introductory and advanced topics’ (Pearson Education India, 2006).
    63. 63)
      • 63. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., et al: ‘Least squares support vector machines’ (World Scientific, Singapore, 2002).
    64. 64)
      • 64. Kohavi, R., et al: ‘A study of cross-validation and bootstrap for accuracy estimation and model selection’. Proc. IJCAI, Stanford, 1995, vol. 14, pp. 11371145.
    65. 65)
      • 65. De Winter, J.C.F.: ‘Using the Student's t-test with extremely small sample sizes’, Pract. Assess. Res. Eval., 2013, 18, (10), pp. 112.
    66. 66)
      • 66. Pachori, R.B., Patidar, S.: ‘Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions’, Comput. Methods Programs Biomed., 2014, 113.2, pp. 494502.
    67. 67)
      • 67. Joshi, V., Pachori, R.B., Vijesh, A.: ‘Classification of ictal and seizure-free EEG signals using fractional linear prediction’, Biomed. Signal Proc. Control, 2014, 9, pp. 15.
    68. 68)
      • 68. Kumar, T.S., Kanhangad, V., Pachori, R.B.: ‘Classification of seizure and seizure-free EEG signals using local binary patterns’, Biomed. Signal Proc. Control, 2015, 15, pp. 3340.
    69. 69)
      • 69. Tiwari, A.K., Pachori, R.B., Kanhangad, V., et al: ‘Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals’, IEEE J. Biomed. Health Inf., 2017, 21, (4), pp. 888896.
    70. 70)
      • 70. Swami, P., Gandhi, T.K., Panigrahi, B.K., et al: ‘A novel robust diagnostic model to detect seizures in electroencephalography’, Expert Syst. Appl., 2016, 56, pp. 116130.
    71. 71)
      • 71. Jaiswal, A.K., Banka, H.: ‘Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals’, Biomed. Signal Proc. Control, 2017, 34, pp. 8192.
    72. 72)
      • 72. Bhati, D., Pachori, R.B., Gadre, V.M.: ‘A novel approach for time–frequency localization of scaling functions and design of three-band biorthogonal linear phase wavelet filter banks’, Digit. Signal Process., 2017, 69, pp. 309322.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-smt.2017.0058
Loading

Related content

content/journals/10.1049/iet-smt.2017.0058
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address