title: The EEG assessment of low-grade hepatic encephalopathy: comparison of an artificial neural network-expert system (ANNES) based evaluation with visual EEG readings and EEG spectral analysis.
abstract: OBJECTIVE
  The EEG provides an objective staging of hepatic encephalopathy (HE), but its interpretation may be biased by inter-observer variability. This study aims at comparing an entirely automatic EEG classification of HE based on an artificial neural network-expert system procedure (ANNES) with visual and spectral analysis based EEG classifications.
  
  
  METHODS
  Two hundred and thirty-eight consecutive cirrhotic patients underwent closed-eye EEG. They were followed up for up to one-year to detect bouts of overt HE and death. The EEG was classified by ANNES, qualitative visual reading, main basic rhythm frequency and spectral analysis. The classifications were assessed on the basis of: (i) match with liver function, (ii) prognostic value and (iii) repeatability.
  
  
  RESULTS
  All classifications were found to be related to the severity of liver failure, with cognitive findings and a history of previous bouts of HE. All of them had prognostic value on the occurrence of overt HE and on survival. The ANNES based classification was more repeatable than the qualitative visual one, and had the advantage of detecting low power EEG, but its efficiency in analyzing low-grade alterations was questionable.
  
  
  CONCLUSIONS
  An entirely automatic - ANNES based - EEG classification of HE can improve the repeatability of EEG assessment, but further improvement of the device is required to classify mild alterations.
  
  
  CONCLUSIONS
  The ANNES based EEG grading of HE needs further improvements to be recommended in clinical practice, but it is already sufficient for detecting normal and clearly altered EEG tracings.
authors.affiliation: Department of Clinical and Experimental Medicine, University of Padova, Padova, Italy. piero.amodio@unipd.it
authors.forename: P
authors.initials: P
authors.lastname: Amodio
authors.forename: A
authors.initials: A
authors.lastname: Pellegrini
authors.forename: E
authors.initials: E
authors.lastname: Ubiali
authors.forename: I
authors.initials: I
authors.lastname: Mathy
authors.forename: F Del
authors.initials: FD
authors.lastname: Piccolo
authors.forename: R
authors.initials: R
authors.lastname: Orsato
authors.forename: A
authors.initials: A
authors.lastname: Gatta
authors.forename: J M
authors.initials: JM
authors.lastname: Guerit
mesh_terms: D004569:Electroencephalography; D005103:Expert Systems; D005260:Female; D006501:Hepatic Encephalopathy / Q000145:classification* / Q000401:mortality / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013057:Spectrum Analysis / Q000379:methods*