Elettroencephalographic staging of hepatic encephalopathy by an artificial neural network and an expert system.

Aim of the study. - To provide an objective EEG assessment of hepatic encephalopathy (HE), we set up and tested an entirely automatic procedure based on an artificial neural network-expert system software (ANNESS). Patients and methods. - A training set sample of 50 EEG (group A) and a test sample of 50 EEG (group B) of 100 cirrhotic patients were considered. The EEGs had been visually classified by an expert electroencephalographer, using a modified five-degree Parsons-Simith classification of HE. The efficiency of the ANNESS, trained in group A, was tested in group B. Results. - Both the ANNESS and the visually-based classifications were found to be correlated to liver insufficiency, as assessed by the Child-Pugh score (Spearman's coefficient ρ = 0.485, P < 0.0001; ρ = 0.489, P < 0.0001, respectively) and by the biochemical indexes of hepatic function (bilirubin: ρ = 0.31 vs. 0.27; albumin: ρ = -0.13 vs. -0.18; prothrombin time ρ = -0.35 vs. -0.52). The classifications were found to be correlated to each other (ρ = 0.84 P < 0.0001, Cohen's kappa = 0.55). However, the ANNESS overestimated grade 2 EEG alterations. Conclusion. - An ANNESS-based classification of EEG in HE provided data comparable with a visually-based classification, except for mild alterations (class 2) that tended to be overestimated. Further optimization of automatic EEG staging of HE is desirable, as well as a prospective clinical evaluation. © 2006 Elsevier SAS. All rights reserved.