Recurrence quantification analysis of EEG predicts responses to incision during anesthesia

  • Authors:

  • Liyu Huang;Weirong Wang;Sekou Singare

  • Affiliations:

  • Department of Biomedical Engineering, Xidian University, Xi'an, China and Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China;Department of Biomedical Engineering, Xidian University, Xi'an, China and Department of Medical Instrumentation, Shanhaidan Hospital, Xi'an, China;Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
  • Year:
  • 2006

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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.