Monitoring Depth of Anesthesia

  • J. W. Huang
  • X.-S. Zhang
  • R. J. Roy
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 96)


This chapter examines the use of complexity analysis, approximate entropy, wavelet transforms, artificial neural networks, fuzzy logic, and neuro-fuzzy method (adaptive network-based fuzzy inference systems) to determine the depth of anesthesia (DOA) of a patient by analyzing mid-latency auditory evoked potentials (MLAEP) and electroencephalograms (EEG). Comparisons are made of the success and computational efficiency of each technique using the data of experimental dogs with different anesthetic modalities.


Artificial Neural Network Fuzzy Logic Mean Arterial Pressure Fuzzy Inference System Adaptive Network Base Fuzzy Inference System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. W. Huang
  • X.-S. Zhang
  • R. J. Roy

There are no affiliations available

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