Time-dependent entropy estimation of EEG rhythm changes following brain ischemia

Ann Biomed Eng. 2003 Feb;31(2):221-32. doi: 10.1114/1.1541013.


Our approach is motivated by the need to generate a rigorous measure of the degree of disorder (or complexity) of the EEG signal in brain injury. Entropy is a method to quantify the order/disorder of a time series. It is the first time that a time-dependent entropy (TDE) is used in the quantification of brain injury level. The TDE was sensitive enough to monitor the significant changes in the subject's brain rhythms during recovery from global ischemic brain injury. Among the different entropy measures, we used Tsallis entropy. This entropy is parametrized and is able to match with the particular properties of EEG, like long-range rhythms, spikes, and bursts. The method was tested in a signal composed of segments of synthetic signals (Gaussian and uniform distributions) and segments of real signals. The real signal segments were extracted from normal EEG, EEG recordings from early recovery, and normal EEG corrupted by simulated spikes and bursts. Adult Wistar rats were subjected to asphyxia-cardiac arrest for 3 and 5 min. The TDE detected the pattern of ischemia-induced EEG alterations and was able to discriminate the different injury levels. Two parameters seem to be good descriptors of the recovery process; the mean entropy and the variance of the estimate followed opposite trends, with the mean entropy decreasing and its variance increasing with injury.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Algorithms
  • Animals
  • Brain Ischemia / classification
  • Brain Ischemia / diagnosis*
  • Brain Ischemia / physiopathology*
  • Electroencephalography / classification
  • Electroencephalography / methods*
  • Humans
  • Male
  • Models, Biological*
  • Models, Statistical*
  • Predictive Value of Tests
  • Rats
  • Rats, Wistar
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Severity of Illness Index
  • Signal Processing, Computer-Assisted