Methodology for epilepsy detection

Entropy
o Measures signal complexity
o EEG with low entropy is due to a small number of dominating processes
o EEG with high entropy is due to a large number of processes
o Relatively simple measure of complexity and system regularity
o Quantifies the predictability of subsequent amplitude values of the EEG based on the knowledge of previous amplitude values
o As a relative measure depends on three parameters
 The length of the epoch
 The length of the compared runs
 The filtering level
o Approximate entropy and Shannon entropy are two entirely different measures
o Approximate entropy measures the predictability of future amplitude values of the EEG based on the one or two previous amplitude values
o Increasing anesthetic concentrations are associated with increasing EEG pattern regularity
o EEG approximate entropy decreases with increasing anesthetic concentration
o At high doses of anesthetics, periods of EEG silence with intermittent bursts of high frequencies occur
o For example median EEG frequency method fail to characterize concentrations because of these bursts
o Brain’s EEG approximation Entropy value is a good candidate for characterizing different extents of cerebral ischemic injury.
 First, in the early stage of ischemia, the EEGs’ approximate entropy difference between ischemic region and normal region increase.
 Second, after ischemia 18 minutes, the approximate entropy of ischemic region become lower than that before ischemia (normal state), which may indicate an emergent injury being induced.
 Last, the approximate entropy of ischemic region (left brain) is lower than that of normal region (right brain).