Skip to main content
SpringerLink
Log in
Book cover

International Conference on Health Information Science

HIS 2017: Health Information Science pp 45–55Cite as

Developing a Tunable Q-Factor Wavelet Transform Based Algorithm for Epileptic EEG Feature Extraction

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10594))

Abstract

Brain signals refer to electroencephalogram (EEG) data that contain the most important information in the human brain, which are non-stationary and nonlinear in nature. EEG signals are a mixture of sustained oscillation and non-oscillatory transients that are difficult to deal with by linear methods. This paper proposes a new technique based on a tunable Q-factor wavelet transform (TQWT) and statistical method (SM), denoted as TQWT-SM, to analyze epileptic EEG recordings. Firstly, EEG signals are decomposed into different sub—bands by the TWQT method, which is parameterized by its Q-factor and redundancy. This approach depends on the resonance of signals, instead of frequency or scales as the Fourier and wavelet transforms do. Secondly, each type of the sub-band vector is divided into n windows, and 10 statistical features from each window are extracted. Finally all the obtained statistical features are forwarded to a k nearest neighbor (k-NN) classifier to evaluate the performance of the proposed TQWT-SM method. The TQWT-SM features extraction method achieves good experimental results for the seven different epileptic EEG binary-categories by the k-NN classifier, in terms of accuracy (Acc), Matthew’s correlation coefficient (MCC), and F score (F1). The outcomes of the proposed technique can assist the experts to detect epileptic seizures.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html.

References

  1. Siuly, S., Li, Y., Wen, P.: Analysis and classification of EEG signals using a hybrid clustering technique. In: 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME), pp. 34–39. IEEE (2010)

    Google Scholar 

  2. Selesnick, I.W.: Resonance-based signal decomposition: a new sparsity-enabled signal analysis method. Sig. Process. 91(12), 2793–2809 (2011)

    Article  Google Scholar 

  3. Zhu, G., Li, Y., Wen, P.P.: Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Comput. Methods Programs Biomed. 115(2), 64–75 (2014)

    Article  Google Scholar 

  4. Kohtoh, S., Taguchi, Y., Matsumoto, N., Wada, M., Huang, Z.L., Urade, Y.: Algorithm for sleep scoring in experimental animals based on fast Fourier transform power spectrum analysis of the electroencephalogram. Sleep Biol. Rhythms 6(3), 163–171 (2008)

    Article  Google Scholar 

  5. Polat, K., Güneş, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007)

    MathSciNet  MATH  Google Scholar 

  6. Murugappan, M., Murugappan, S., Gerard, C.: Wireless EEG signals based neuromarketing system using Fast Fourier Transform (FFT). In: 2014 IEEE 10th International Colloquium on Signal Processing & its Applications (CSPA), pp. 25–30. IEEE (2014)

    Google Scholar 

  7. Samar, V.J., Bopardikar, A., Rao, R., Swartz, K.: Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain Lang. 66(1), 7–60 (1999)

    Article  Google Scholar 

  8. Subasi, A., Alkan, A., Koklukaya, E., Kiymik, M.K.: Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Networks 18(7), 985–997 (2005)

    Article  Google Scholar 

  9. Zhang, Y., Liu, B., Ji, X., Huang, D.: Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Process. Lett. 45(2), 1–14 (2016)

    Google Scholar 

  10. Lekshmi, S., Selvam, V., Rajasekaran, M.P.: EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network. In: 2014 International Conference on Communications and Signal Processing (ICCSP), pp. 687–690. IEEE (2014)

    Google Scholar 

  11. Gajic, D., Djurovic, Z., Di Gennaro, S., Gustafsson, F.: Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed. Eng. Appl. Basis Commun. 26(02), 1450021 (2014)

    Article  Google Scholar 

  12. Pritchard, W.S., Duke, D.W., Krieble, K.K.: Dimensional analysis of resting human EEG II: surrogate-data testing indicates nonlinearity but not low-dimensional chaos. Psychophysiology 32(5), 486–491 (1995)

    Article  Google Scholar 

  13. Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)

    Article  Google Scholar 

  14. Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput. Methods Programs Biomed. 109(3), 339–345 (2013)

    Article  Google Scholar 

  15. Acharya, U.R., Sudarshan, V.K., Adeli, H., Santhosh, J., Koh, J.E., Puthankatti, S.D., Adeli, A.: A novel depression diagnosis index using nonlinear features in EEG signals. Eur. Neurol. 74(1–2), 79–83 (2015)

    Article  Google Scholar 

  16. Pachori, R.B., Patidar, S.: Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput. Methods Programs Biomed. 113(2), 494–502 (2014)

    Article  Google Scholar 

  17. Broberg, R., Lewis, R.: Classification of epileptoid oscillations in EEG using Shannon’s entropy amplitude probability distribution. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds.) SISAP 2014. LNCS, vol. 8821, pp. 247–252. Springer, Cham (2014). doi:10.1007/978-3-319-11988-5_23

    Google Scholar 

  18. Jie, X., Cao, R., Li, L.: Emotion recognition based on the sample entropy of EEG. Bio-Med. Mater. Eng. 24(1), 1185–1192 (2014)

    Google Scholar 

  19. Patidar, S., Pachori, R.B., Upadhyay, A., Acharya, U.R.: An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Appl. Soft Comput. 50, 71–78 (2017)

    Article  Google Scholar 

  20. Patidar, S., Panigrahi, T.: Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed. Signal Process. Control 34, 74–80 (2017)

    Article  Google Scholar 

  21. Patidar, S., Pachori, R.B., Acharya, U.R.: Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowl.-Based Syst. 82, 1–10 (2015)

    Article  Google Scholar 

  22. Hassan, A.R., Siuly, S., Zhang, Y.: Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput. Methods Programs Biomed. 137, 247–259 (2016)

    Article  Google Scholar 

  23. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: 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 64(6), 061907 (2001)

    Article  Google Scholar 

  24. Al Ghayab, H.R., Li, Y., Abdulla, S., Diykh, M., Wan, X.: Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Inform. 3(2), 85–91 (2016)

    Article  Google Scholar 

  25. Bayram, I., Selesnick, I.W.: Frequency-domain design of overcomplete rational-dilation wavelet transforms. IEEE Trans. Signal Process. 57(8), 2957–2972 (2009)

    Article  MathSciNet  Google Scholar 

  26. Selesnick, I.W.: Wavelet transform with tunable Q-factor. IEEE Trans. Signal Process. 59(8), 3560–3575 (2011)

    Article  MathSciNet  Google Scholar 

  27. Bhattacharyya, A., Pachori, R.B., Upadhyay, A., Acharya, U.R.: Tunable-Q wavelet transform based multiscale entropy measure for automated classification of Epileptic EEG signals. Appl. Sci. 7(4), 385 (2017)

    Article  Google Scholar 

  28. Nguyen-Ky, T., Wen, P., Li, Y., Malan, M.: Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques. Comput. Biol. Med. 42(6), 680–691 (2012)

    Article  Google Scholar 

  29. Siuly, S., Kabir, E., Wang, H., Zhang, Y.: Exploring sampling in the detection of multicategory EEG signals. In: Computational and Mathematical Methods in Medicine 2015 (2015)

    Google Scholar 

  30. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)

    MATH  Google Scholar 

  31. Ergen, B.: Scale invariant and fixed-length feature extraction by integrating discrete cosine transform and autoregressive signal modeling for palmprint identification. Turk. J. Electr. Eng. Comput. Sci. 24(3), 1768–1781 (2016)

    Article  Google Scholar 

  32. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

  33. Siuly, S., Li, Y.: Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Comput. Methods Programs Biomed. 119(1), 29–42 (2015)

    Article  Google Scholar 

  34. Siuly, S., Li, Y., Wen, P.: Identification of motor imagery tasks through CC–LR algorithm in brain computer interface. Int. J. Bioinform. Res. Appl. 9(2), 156–172 (2013)

    Article  Google Scholar 

  35. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)

    MathSciNet  Google Scholar 

  36. Azar, A.T., El-Said, S.A.: Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Health, Engineering and Sciences, University of Southern Queensland, Darling Heights, QLD, 4350, Australia

    Hadi Ratham Al Ghayab, Yan Li & Paul Wen

  2. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia

    Siuly

  3. Open Access College, Language Centre, University of Southern Queensland, Darling Heights, QLD, 4350, Australia

    Shahab Abdulla

Authors
  1. Hadi Ratham Al Ghayab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siuly
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shahab Abdulla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paul Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hadi Ratham Al Ghayab .

Editor information

Editors and Affiliations

  1. Centre for Applied Informatics, Victoria University, Melbourne, Victoria, Australia

    Siuly Siuly

  2. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  3. University of Nottingham, Nottingham, United Kingdom

    Uwe Aickelin

  4. Swinburne University of Technology, Melbourne, Victoria, Australia

    Rui Zhou

  5. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

  7. Institute for High Energy Physics, Protvino, Russia

    Stanislav Klimenko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Al Ghayab, H.R., Li, Y., Siuly, Abdulla, S., Wen, P. (2017). Developing a Tunable Q-Factor Wavelet Transform Based Algorithm for Epileptic EEG Feature Extraction. In: Siuly, S., et al. Health Information Science. HIS 2017. Lecture Notes in Computer Science(), vol 10594. Springer, Cham. https://doi.org/10.1007/978-3-319-69182-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69182-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69181-7

  • Online ISBN: 978-3-319-69182-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation