Electroencephalographic staging of hepatic encephalopathy by an artificial neural network and an expert system.
OBJECTIVE
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).


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 rho=0.485, P<0.0001; rho=0.489, P<0.0001, respectively) and by the biochemical indexes of hepatic function (bilirubin: rho=0.31 vs. 0.27; albumin: rho=-0.13 vs. -0.18; prothrombin time rho=-0.35 vs. -0.52). The classifications were found to be correlated to each other (rho=0.84 P<0.0001, Cohen's kappa=0.55). However, the ANNESS overestimated grade 2 EEG alterations.


CONCLUSIONS
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.
Pellegrini
A
A
Clinica Medica 5, Università di Padova, Viale Giustiniani 2, 35128 Padova, Italy.
Ubiali
E
E
Orsato
R
R
Schiff
S
S
Gatta
A
A
Castellaro
A
A
Casagrande
A
A
Amodio
P
P
D000368:Aged; D004502:Education, Medical, Continuing; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006501:Hepatic Encephalopathy / Q000209:etiology / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer