Using back propagation feedback neural networks and recurrence quantification analysis of EEGs predict responses to incision during anesthesia

Authors:
Liyu Huang;Weirong Wang;Sekou Singare
Affiliations:
Department of Biomedical Engineering, Xidian University, Xi'an, China;Department of Biomedical Engineering, Xidian University, Xi'an, China;Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Physica D

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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.