Subject(s)
Algorithms , Blood Pressure Determination/methods , Signal Processing, Computer-Assisted , Adult , Age Factors , Aged , Blood Pressure , Blood Pressure Determination/instrumentation , Female , Humans , Male , Manometry/instrumentation , Manometry/methods , Middle Aged , Pulse , Tilt-Table TestABSTRACT
Records of brain electrical activity from intracranial EEG of four patients with different types of epilepsy are analyzed to predict the epileptic seizure onset. A method based on the evolution of the accumulated energy using wavelet analysis is introduced. This is an efficient method to predict epileptic seizures: from 13 preseizure signals, the seizure onset in 12 of those are predicted.
Subject(s)
Brain/physiology , Electroencephalography/methods , Energy Metabolism/physiology , Epilepsy/diagnosis , Algorithms , Brain Mapping , Electrodes , Epilepsy/classification , Epilepsy/physiopathology , Humans , Predictive Value of Tests , Sensitivity and Specificity , Time FactorsABSTRACT
This study deals with the problem of identification of epileptic events in electroencephalograms using multiresolution wavelet analysis. The following problems are analyzed: time localization and characterization of epileptiform events, and computational efficiency of the method. The algorithm presented is based on a polynomial spline wavelet transform. The multiresolution representation obtained from this wavelet transform and the corresponding digital filters derived allows time localization of epileptiform activity. The proposed detector is based on the multiresolution energy function. Electroencephalogram records from epileptic patients were analyzed, and results obtained are shown. Some comparisons with other methods are given.