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Using Back Propagation Feedback Neural Networks and Recurrence Quantification Analysis of EEGs Predict Responses to Incision During Anesthesia

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

Abstract

This paper presents a new approach to detect depth of anaesthesia by using recurrence quantification analysis of electroencephalogram (EEG) and artificial neural network(ANN) . From 98 consenting patient experiments, 98 distinct EEG recordings were collected prior to incision during isoflurane anaesthesia of different levels. The seven measures of recurrence plot were extracted from each of four-channel EEG time series. Prediction was made by means of ANN. Training and testing the ANN used the ‘leave-one-out’ method. The prediction was tested by monitoring the responses to incision. The system was able to correctly classify purposeful responses in average accuracy of 92.86% of the cases. This method is also computationally fast and acceptable real-time clinical performance was obtained.

Keywords

Recurrence Plot Recurrence Quantification Analysis Bispectral Analysis Spectral Edge Frequency Sponse State 
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 2006

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringXidian UniversityXi’anChina
  2. 2.Institute of Biomedical EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.Department of Medical InstrumentationShanhaidan HospitalXi’anChina

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