Advertisement

A New Adaptive Signal Segmentation Approach Based on Hiaguchi’s Fractal Dimension

  • Hamed Azami
  • Alireza Khosravi
  • Milad Malekzadeh
  • Saeid Sanei
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

In many non-stationary signal processing applications such as electroencephalogram (EEG), it is better to divide the signal into smaller segments during which the signals are pseudo-stationary. Therefore, they can be considered stationary and analyzed separately. In this paper a new segmentation method based on discrete wavelet transform (DWT) and Hiaguchi’s fractal dimension (FD) is proposed. Although the Hiaguchi’s algorithm is the most accurate algorithms to obtain an FD for EEG signals, the algorithm is very sensitive to the inherent existing noise. To overcome the problem, we use the DWT to reduce the artifacts such as electrooculogram (EOG) and electromyogram (EMG) which often occur in higher frequency bands. In order to evaluate the performance of the proposed method, it is applied to a synthetic and real EEG signals. The simulation results show the Hiaguchi’s FD with DWT can accurately detect the signal segments.

Keywords

Non-stationary signal adaptive segmentation discrete wavelet transform Hiaguchi’s fractal dimension 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Azami, H., Sanei, S., Mohammadi, K.: A Novel Signal Segmentation Method Based on Standard Deviation and Variable Threshold. Journal of Computer Applications 34(2), 27–34 (2011)Google Scholar
  2. 2.
    Azami, H., Bozorgtabar, B., Shiroie, M.: Automatic signal segmentation using the fractal dimension and weighted moving average filter. Journal of Electrical & Computer science 11(6), 8–15 (2011)Google Scholar
  3. 3.
    Agarwal, R., Gotman, J.: Adaptive Segmentation of Electroencephalographic Data Using a Nonlinear Energy Operator. In: IEEE International Symposium on Circuits and Systems (ISCAS 1999), vol. 4, pp. 199–202 (1999)Google Scholar
  4. 4.
    Hassanpour, H., Mesbah, M., Boashash, B.: Time-Frequency Based Newborn EEG Seizure Detection Using Low and High Frequency Signatures. Physiological Measurement 25, 935–944 (2004)CrossRefGoogle Scholar
  5. 5.
    Hassanpour, H., Mesbah, M., Boashash, B.: Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-based Techniques. EURASIP Journal on Applied Signal Processing 16, 2544–2554 (2004)Google Scholar
  6. 6.
    Kosar, K., Lhotská, L., Krajca, V.: Classification of Long-Term EEG Recordings. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds.) ISBMDA 2004. LNCS, vol. 3337, pp. 322–332. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Kirlangic, M.E., Perez, D., Kudryavtseva, S., Griessbach, G., Henning, G., Ivanova, G.: Fractal Dimension as a Feature for Adaptive Electroencephalogram Segmentation in Epilepsy. In: IEEE International EMBS Conference, vol. 2, pp. 1573–1576 (2001)Google Scholar
  8. 8.
    Azami, H., Mohammadi, K., Bozorgtabar, B.: An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter. Journal of Signal and Information Processing 3(1), 39–44 (2012)CrossRefGoogle Scholar
  9. 9.
    Azami, H., Mohammadi, K., Hassanpour, H.: An Improved Signal Segmentation Method Using Genetic Algorithm. Journal of Computer Applications 29(8), 5–9 (2011)CrossRefGoogle Scholar
  10. 10.
    Hassanpour, H., Shahiri, M.: Adaptive Segmentation Using Wavelet Transform. In: International Conference on Electrical Engineering, pp. 1–5 (April 2007)Google Scholar
  11. 11.
    Gao, J., Sultan, H., Hu, J., Tung, W.W.: Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: a Comparison. IEEE Signal Processing Letters 17(3), 237–240 (2010)CrossRefGoogle Scholar
  12. 12.
    Hsu, W.Y., Lin, C.C., Ju, M.S., Sun, Y.N.: Wavelet-Based Fractal Features with Active Segment Selection: Application to Single-Trial EEG Data. Elsevier Journal of Neuroscience Methods 163(1), 145–160 (2007)CrossRefGoogle Scholar
  13. 13.
    Asaduzzaman, K., Reaz, M.B.I., Mohd-Yasin, F., Sim, K.S., Hussain, M.S.: A Study on Discrete Wavelet-Based Noise Removal from EEG Signals. Journal of Advances in Experimental Medicine and Biology 680, 593–599 (2010)CrossRefGoogle Scholar
  14. 14.
    Estrada, E., Nazeran, H., Sierra, G., Ebrahimi, F., Setarehdan, S.K.: Wavelet-Based EEG Denoising for Automatic Sleep Stage Classification. In: International Conference on Electrical Communications and Computers (CONIELECOMP), pp. 295–298 (2011)Google Scholar
  15. 15.
    Geetha, G., Geethalakshmi, S.N.: EEG De-noising Using Sure Thresholding Based on Wavelet Transforms. International Journal of Computer Applications 24(6) (2011)Google Scholar
  16. 16.
    Easwaramoorthy, D., Uthayakumar, R.: Analysis of Biomedical EEG Signals Using Wavelet Transforms and Multifractal Analysis. In: IEEE International Conference on Communication Control and Computing Technologies (ICCCCT), pp. 545–549 (2010)Google Scholar
  17. 17.
    Tao, Y., Lam, E.C.M., Tang, Y.Y.: Feature Extraction Using Wavelet and Fractal. Elsevier Journal of Pattern Recognition 22(3-4), 271–287 (2001)zbMATHGoogle Scholar
  18. 18.
    Rajagopalan, S., Aller, J.M., Restrepo, J.A., Habetler, T.G., Harley, R.G.: Analytic-Wavelet-Ridge-Based Detection of Dynamic Eccentricity in Brushless Direct Current (BLDC) Motors Functioning Under Dynamic Operating Conditions. IEEE Transaction on Industrial Electronics 54(3), 1410–1419 (2007)CrossRefGoogle Scholar
  19. 19.
    Gunasekaran, S., Revathy, K.: Fractal Dimension Analysis of Audio Signals for Indian Musical Instrument Recognition. In: International Conference on Audio, Language and Image Processing (ICALIP), pp. 257–261 (2008)Google Scholar
  20. 20.
    Acharya, U.R., Faust, O., Kannathal, N., Chua, T., Laxminarayan, S.: Non-Linear Analysis of EEG Signals at Various Sleep Stages. Computer Methods and Programs in Biomedicine 80(1), 37–45 (2005)CrossRefGoogle Scholar
  21. 21.
    Esteller, R., Vachtsevanos, G., Echauz, J., Litt, B.: A Comparison of Fractal Dimension Algorithms Using Synthetic and Experimental Data. In: IEEE International Symposium on Circuits and Systems (ISCAS 1999), vol. 3, pp. 199–202 (1999)Google Scholar
  22. 22.
    Esteller, R., Vachtsevanos, G., Echauz, J., Litt, B.: A Comparison of Waveform Fractal Dimension Algorithms. IEEE Transaction on Circuits and Systems 48(2), 177–183 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hamed Azami
    • 1
  • Alireza Khosravi
    • 2
  • Milad Malekzadeh
    • 2
  • Saeid Sanei
    • 3
  1. 1.Department of Electrical EngineeringIran University of Science and TechnologyIran
  2. 2.Department of Electrical and Computer EngineeringBabol Industrial UniversityIran
  3. 3.Department of ComputingUniversity of SurreyUK

Personalised recommendations