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International Conference on Intelligent Computing

ICIC 2012: Emerging Intelligent Computing Technology and Applications pp 152–159Cite as

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

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Part of the book series: Communications in Computer and Information Science ((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.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, Iran

    Hamed Azami

  2. Department of Electrical and Computer Engineering, Babol Industrial University, Iran

    Alireza Khosravi & Milad Malekzadeh

  3. Department of Computing, University of Surrey, UK

    Saeid Sanei

Authors
  1. Hamed Azami
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  2. Alireza Khosravi
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  3. Milad Malekzadeh
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  4. Saeid Sanei
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Azami, H., Khosravi, A., Malekzadeh, M., Sanei, S. (2012). A New Adaptive Signal Segmentation Approach Based on Hiaguchi’s Fractal Dimension. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_22

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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