TY - GEN
T1 - Wavelet-based texture analysis of EEG signal for prediction of epileptic seizures
AU - Petrosian, Arthur
AU - Homan, Richard
AU - Pemmaraju, Suryalakshmi
AU - Mitra, Sunanda
PY - 1995
Y1 - 1995
N2 - 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 dimension. 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 recording. The purpose of this study is to investigate whether wavelet transform can be used to further enhance the developed methods for prediction of epileptic seizures. The combined consideration of texture and entropy characteristics extracted from subsignals decomposed by wavelet transform are explored for that purpose. Yet, the novel neuro-fuzzy clustering algorithm is performed on wavelet coefficients to segment given EEG recording into different stages prior to an actual seizure onset.
AB - 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 dimension. 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 recording. The purpose of this study is to investigate whether wavelet transform can be used to further enhance the developed methods for prediction of epileptic seizures. The combined consideration of texture and entropy characteristics extracted from subsignals decomposed by wavelet transform are explored for that purpose. Yet, the novel neuro-fuzzy clustering algorithm is performed on wavelet coefficients to segment given EEG recording into different stages prior to an actual seizure onset.
UR - http://www.scopus.com/inward/record.url?scp=0029543214&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029543214
SN - 0819419281
SN - 9780819419286
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 189
EP - 194
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Laine, Andrew F.
A2 - Unser, Michael A.
A2 - Wickerhauser, Mladen V.
T2 - Wavelet Applications in Signal and Image Processing III. Part 1 (of 2)
Y2 - 12 July 1995 through 14 July 1995
ER -