Abstract
In many signal processing applications, it is often needed to segment signals into small epochs with similar characteristics such as amplitude and/or frequency that are particularly meaningful to clinicians and for assessment by neurophysiologists. This paper presents a novel adaptive segmentation method based on the time-varying autoregressive (TVAR) model, integral, and basic generalized likelihood ratio (GLR). Since autoregressive (AR) model for the GLR method is valid for only stationary signals, TVAR is employed as a valuable and powerful tool for non-stationary signals. Moreover, to improve the performance of the basic GLR and increase its speed, we propose to use moving steps more than one sample for successive windows in the basic GLR method. Performance of the proposed method is compared with existing GLR and wavelet GLR (WGLR) methods using both the synthetic signal and real EEG data. The simulation results indicate the high accuracy of the proposed method.
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Acknowledgment
The authors wish to thank Prof. Saeid Sanei in University of Surrey, UK and Prof. William D. Penny in University College London, UK, for their so valuable and kind guidance.
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Azami, H., Anisheh, S.M., Hassanpour, H. (2014). An Adaptive Automatic EEG Signal Segmentation Method Based on Generalized Likelihood Ratio. In: Movaghar, A., Jamzad, M., Asadi, H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-10849-0_18
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DOI: https://doi.org/10.1007/978-3-319-10849-0_18
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