A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy

被引:485
作者
Adeli, Hojjat
Ghosh-Dastidar, Samanwoy
Dadmehr, Nahid
机构
[1] Ohio State Univ, Dept Biomed Engn, Columbus, OH 43210 USA
[2] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[3] Ohio State Univ, Dept Civil & Environm Engn & Geodet Sci, Columbus, OH 43210 USA
[4] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA
[5] Ohio State Univ, Dept Neurosci, Columbus, OH 43210 USA
关键词
chaos theory; EEG subbands; electroencephalogram (EEG); epilepsy; wavelet transform;
D O I
10.1109/TBME.2006.886855
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma subbands of EEGs for detection of seizure and epilepsy. The nonlinear dynamics of the original EEGs are quantified in the form of the correlation dimension (CD, representing system complexity) and the largest Lyapunov exponent (LLE, representing system chaoticity). The new wavelet-based methodology isolates the changes in CD and LLE in specific subbands of the EEG. The methodology is applied to three different groups of EEG signals: 1) healthy subjects; 2) epileptic subjects during a seizure-free interval (interictal EEG); 3) epileptic subjects during a seizure (ictal EEG). The effectiveness of CD and LLE in differentiating between the three groups is investigated based on statistical significance of the differences. It is observed that while there may not be significant differences in the values of the parameters obtained from the original EEG, differences may be identified when the parameters are employed in conjunction with specific EEG subbands. Moreover, it is concluded that for the higher frequency beta and gamma subbands, the CD differentiates between the three groups, whereas for the lower frequency alpha subband, the LLE differentiates between the three groups.
引用
收藏
页码:205 / 211
页数:7
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