1995
DOI: 10.1117/12.217574
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<title>Wavelet-based texture analysis of EEG signal for prediction of epileptic seizure</title>

Abstract: Electroencephalographic (EEG) signal texture content analysis has been proposed for early warning of an epileptic seizure'. This approach was evaluated by investigating the interrelationship between texture features and basic signal informational characteristics, such as Kolmogorov complexity and fractal dimension2. The comparison of several traditional techniques, including higher-order FIR digital filtering, chaos, autoregressive and FFT time-frequency analysis was also carried out on the same epileptic EEG … Show more

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Cited by 7 publications
(3 citation statements)
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“…This technique was further enhanced using directionally decomposed low-pass and high-pass subbands obtained by wavelet transform [6]. Multiresolution wavelet representation [15,16] provides a simple hierarchical framework for interpreting the signal information.…”
Section: Eeg Segmentation and Wavelet Decompositionmentioning
confidence: 99%
See 2 more Smart Citations
“…This technique was further enhanced using directionally decomposed low-pass and high-pass subbands obtained by wavelet transform [6]. Multiresolution wavelet representation [15,16] provides a simple hierarchical framework for interpreting the signal information.…”
Section: Eeg Segmentation and Wavelet Decompositionmentioning
confidence: 99%
“…Those limitations of the related mathematical algorithms are based on the requirements of long-term stationarity of the time series (which is often not the case in epileptic BEG considerations) and an extremely high sensitivity to the noise, both electrical and physiologic in its nature. Our efforts were therefore directed to constructing alternative measures, which could reflect short-term signal "textural" complexity conditions which may then result in seizures [5][6][7]. In [6] we showed the feasibility of using signal local texture features in conjunction with a wavelet transform.…”
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confidence: 98%
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