title: Internal representation in neural networks used for classification of patient anaesthetic states and dosage.
abstract: In this study we aimed to explore the ability of artificial neural networks (ANN) to classify patient anaesthetic states and dosage. Surgical data obtained under different states of anaesthesia and dose levels were modelled via this approach. It is shown that inferential parameters can be used to determine the patient anaesthetic states and drug dosage. In addition to demonstrating the capability of ANN for classification we were interested in the internal representations that are developed automatically by networks while they are learning their processing task. An unsupervised learning procedure of clustering via which the classes are inferred from the data and a supervised learning technique of discrimination via which to construct a classification of the known categories were applied to analyse the performance of the ANN. Discriminant analysis (DA) was also utilised to optimise the network architecture.
authors.affiliation: Department of Automatic Control and Systems Engineering, The University of Sheffield, UK.
authors.forename: L
authors.initials: L
authors.lastname: Vefghi
authors.forename: D A
authors.initials: DA
authors.lastname: Linkens
mesh_terms: D000758:Anesthesia / Q000145:classification*; D000777:Anesthetics / Q000494:pharmacology*; D004305:Dose-Response Relationship, Drug; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer
chemical_list: D000777:Anesthetics