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Recurrence Quantification Analysis of EEG Predicts Responses to Incision During Anesthesia

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Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

The need for assessing the depth of anesthesia during surgical operations has existed since the introduction of anaesthesia, but sufficiently reliable method is still not found. This paper presents a new approach to detect depth of anaesthesia by using recurrence quantification analysis of electroencephalogram (EEG) and artificial neural network(ANN). The EEG recordings were collected from consenting patient prior to incision during isoflurane anaesthesia of different levels. The four measures of recurrence plot were extracted from each of eight-channel EEG time series. Prediction was made by means of ANN. The system was able to correctly classify purposeful responses in average accuracy of 87.76% of the cases.

Keywords

Artificial Neural Network Recurrence Plot Recurrence Quantification Analysis Recurrence Point Bispectral Analysis 
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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