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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| Published in: | Clinical neurophysiology Vol. 117; no. 10; pp. 2243 - 2251 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Elsevier Ireland Ltd
01.10.2006
Elsevier |
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| ISSN: | 1388-2457, 1872-8952 |
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| Abstract | 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. |
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| AbstractList | 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. 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.OBJECTIVEThe 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.METHODSTwo 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.RESULTSAll 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.CONCLUSIONSAn 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.SIGNIFICANCEThe 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. |
| Author | Amodio, P. Mathy, I. Orsato, R. Gatta, A. Guerit, J.M. Ubiali, E. Piccolo, F. Del Pellegrini, A. |
| Author_xml | – sequence: 1 givenname: P. surname: Amodio fullname: Amodio, P. email: piero.amodio@unipd.it organization: Department of Clinical and Experimental Medicine, University of Padova, Padova, Italy – sequence: 2 givenname: A. surname: Pellegrini fullname: Pellegrini, A. organization: Department of Clinical and Experimental Medicine, University of Padova, Padova, Italy – sequence: 3 givenname: E. surname: Ubiali fullname: Ubiali, E. organization: Neurofisiologia Clinica, Ospedali Riuniti di Bergamo, Italy – sequence: 4 givenname: I. surname: Mathy fullname: Mathy, I. organization: Clinical Neurophysiology Unit, Cliniques Universitaires Saint Luc, University of Louvain, Brusselles, Belgium – sequence: 5 givenname: F. Del surname: Piccolo fullname: Piccolo, F. Del organization: Department of Clinical and Experimental Medicine, University of Padova, Padova, Italy – sequence: 6 givenname: R. surname: Orsato fullname: Orsato, R. organization: Micromed Research Unit, Mogliano Veneto, Italy – sequence: 7 givenname: A. surname: Gatta fullname: Gatta, A. organization: Department of Clinical and Experimental Medicine, University of Padova, Padova, Italy – sequence: 8 givenname: J.M. surname: Guerit fullname: Guerit, J.M. organization: Clinical Neurophysiology Unit, Cliniques Universitaires Saint Luc, University of Louvain, Brusselles, Belgium |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| 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 |
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