Comparison of neural networks, fuzzy and stochastic prediction models for return of consciousness after general anesthesia

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

Catarina S Nunes (Author) FCUP; Teresa F Mendonca (Author) FCUP; Pedro Amorim (Author) Other; David A Ferreira (Author) Other; Luis Antunes (Author) Other

Conference proceedings International

Title: IEEE Conference on Decision and Control

Pages: 4827-4832

44th IEEE Conference on Decision Control/European Control Conference (CCD-ECC)

Seville, SPAIN, DEC 12-15, 2005

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 6 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-000-6ZF

DOI: 10.1109/cdc.2005.1582925

Abstract (EN): This paper presents three modeling techniques to predict return of consciousness (ROC) after general anesthesia, considering the effect concentration of the anesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anesthetic and the analgesic mean dose during surgery, and the age of the patient, proved to have significantly high correlation coefficients. Variables like the mean bispectral index value during surgery, duration of surgery did not present a statistical relation with ROC. Stochastic regression models were built using the variables with higher correlation. Secondly, fuzzy models were built using an Adaptive Network-Based Fuzzy Inference System (ANFIS) also relating different sets of variables. Thirdly, radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anesthetic drug effect concentration at awakening. Clinical data was used to train and test the models. The stochastic models and the fuzzy models proved to have good prediction properties. The RBF network models were more biased towards the training set. The best balanced performance was achieved with the fuzzy models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6