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

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...

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Veröffentlicht in:Clinical neurophysiology Jg. 117; H. 10; S. 2243 - 2251
Hauptverfasser: Amodio, P., Pellegrini, A., Ubiali, E., Mathy, I., Piccolo, F. Del, Orsato, R., Gatta, A., Guerit, J.M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ireland Ltd 01.10.2006
Elsevier
Schlagworte:
Artificial neural network
Automatic EEG analysis
Biological and medical sciences
Cirrhosis
EEG
Electrodiagnosis. Electric activity recording
Electroencephalography
Expert Systems
Eye and associated structures. Visual pathways and centers. Vision
Female
Fundamental and applied biological sciences. Psychology
Hepatic encephalopathy
Hepatic Encephalopathy - classification
Hepatic Encephalopathy - mortality
Hepatic Encephalopathy - physiopathology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Middle Aged
Nervous system
Neural Networks (Computer)
Prognosis
Reproducibility of Results
Sensitivity and Specificity
Spectral analysis
Spectrum Analysis - methods
Staging
Vertebrates: nervous system and sense organs
Visual reading
Cirrhosis
Automatic EEG analysis
Staging
EEG
Artificial neural network
Visual reading
Hepatic encephalopathy
Spectral analysis
Automatic classification
Variability
Electrophysiology
Hepatic disease
Electroencephalography
Eye
Visual system
Encephalopathy
Observer
Human
Evaluation
Nervous system diseases
Rhythm
Neural network
Interpretation
Cerebral disorder
Repeatability
Central nervous system disease
Digestive diseases
ISSN:1388-2457, 1872-8952
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Zusammenfassung: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. 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. 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. 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. 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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2006.06.714