Ten lectures on wavelets
Computers in Biology and Medicine
Application of wavelet-based similarity analysis to epileptic seizures prediction
Computers in Biology and Medicine
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm
Computers in Biology and Medicine
A wavelet-based estimating depth of anesthesia
Engineering Applications of Artificial Intelligence
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The analysis of generalized tonic-clonic seizures is usually difficult with quantitative EEG techniques due to muscle artifact. We applied two quantifiers based on the Wavelet Transform to evaluate 20 seizures from eight consecutive patients admitted for video-EEG monitoring. We studied the relative wavelet energy and the wavelet entropy over time. In 16/20 events we found significant decremental activity in the relative wavelet energy associated with frequency band 0.8-3.2 Hz (delta activity) at the seizure onset, indicating that the seizure is dominated by medium frequency bands 3.2-12.8 Hz (theta and alpha bands). In 19/20 events the mean wavelet entropy presents lower values during the ictal period compared to the preictal period, indicating that the associated dynamic is more ordered and simple. Thus the employed tools show good accuracy for detecting changes in the system dynamic. We conclude that this behavior could be induced by frequency tuning in the neuronal activity triggered by an hypothetic epileptic focus.