Wavelet-Based Fractal Analysis of the Epileptic EEG Signal

被引：3
|
作者：
Janjarasjitt, Suparerk [1 ]
Loparo, Kenneth A. [2 ]
机构：
[1] Ubon Ratchathani Univ, Dept Elect & Elect Engn, Khon Kaen, Thailand
[2] Case Western Reserve Univ, Dept Elect & Comp Engn, Cleveland, OH 44106 USA
来源：
2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS 2009) | 2009年
关键词：
NONLINEAR DYNAMICAL ANALYSIS; SLEEP STAGES; TIME-SERIES; COMPLEXITY; DIMENSION;
D O I：
10.1109/ISPACS.2009.5383886
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The wavelet transform is a natural tool for characterizing self-similar signals. In this work, the spectral exponent gamma derived from the wavelet-based representation for 1/f processes is used to investigate the self-similarity of electrocorticography (intracranial EEG) signals from an epilepsy patient. An increase in gamma leads to sample signals with smoother temporal patterns. Our computational results show that during an epileptic seizure gamma is significantly higher than that associated with other states of the brain, implying that wavelet-based fractal analysis is potentially a useful computational tool for epileptic seizure detection.
引用
收藏
页码：127 / +
页数：3
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