الفهرس 
EEG Spike Detection
Time-Domain Spike DetectionOnly 14 pages are availabe for public view
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Abstract
Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. According to World Health Organization WHO, approximately, 50 million people worldwide have epilepsy, making it one of the most common neurological diseases. Electroencephalogram (EEG) is a test that measures and records the electrical activities of the brain, and is widely used in the prediction and analysis of epileptic seizures. This work provides a statistical analysis associated with EEG signals assuming that these signals can be categorized into inter-ictal, pre-ictal, and ictal states. Also, this work provides a simple approach for making a good
prediction for seizure patients to help them saving their life.This work studies histograms and cumulative histograms for segments of various signal states, in wavelet domain by utilizing different signal processing tools such as the differentiator and median filtering, as well as the local mean, and local variance estimators.The results show that signal states can be distinguished according to statistics in the wavelet domain. Also, compressive sensing has been investigated with epileptic seizure prediction as a tool to reduce communication resources and processing time consumption. Simulation results enforced the possibility to use compressive sensing opening the doo
for more compact seizure prediction algorithms.