Monitoring the depth of anesthesia using entropy features and an artificial neural network.

@article{Shalbaf2013MonitoringTD,
  title={Monitoring the depth of anesthesia using entropy features and an artificial neural network.},
  author={Reza Shalbaf and Hamid Behnam and Jamie W. Sleigh and Alistair Steyn-Ross and Logan James Voss},
  journal={Journal of neuroscience methods},
  year={2013},
  volume={218 1},
  pages={17-24}
}
Monitoring the depth of anesthesia using an electroencephalogram (EEG) is a major ongoing challenge for anesthetists. The EEG is a recording of brain electrical activity, and it contains valuable information related to the different physiological states of the brain. This study proposes a novel automated method consisting of two steps for assessing anesthesia depth. Initially, the sample entropy and permutation entropy features were extracted from the EEG signal. Because EEG-derived parameters… CONTINUE READING

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