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.
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Huang, L., Wang, W., Singare, S. (2006). Recurrence Quantification Analysis of EEG Predicts Responses to Incision During Anesthesia. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_7
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DOI: https://doi.org/10.1007/11893295_7
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