title: New spectral thresholds improve the utility of the electroencephalogram for the diagnosis of hepatic encephalopathy.
abstract: OBJECTIVE
  The utility of the electroencephalogram (EEG) for the diagnosis of hepatic encephalopathy, using conventional spectral thresholds, is open to question. The aim of this study was to optimise its diagnostic performance by defining new spectral thresholds.
  
  
  METHODS
  EEGs were recorded in 69 healthy controls and 113 patients with cirrhosis whose neuropsychiatric status was classified using clinical and psychometric criteria. New EEG spectral thresholds were calculated, on the parietal P3-P4 lead derivation, using an extended multivariable receiver operating characteristic curve analysis. Thresholds were validated in a separate cohort of 68 healthy controls and 113 patients with cirrhosis. The diagnostic performance of the newly derived spectral thresholds was further validated using a machine learning technique.
  
  
  RESULTS
  The diagnostic performance of the new thresholds (sensitivity 75.0%; specificity 77.4%) was better balanced than that of the conventional thresholds (58.3%; 93.2%) and comparable to the performance of a machine learning technique (72.9%; 76.8%). The diagnostic utility of the new thresholds was confirmed in the validation cohort.
  
  
  CONCLUSIONS
  Adoption of the new spectral thresholds would significantly improve the utility of the EEG for the diagnosis of hepatic encephalopathy.
  
  
  CONCLUSIONS
  These new spectral EEG thresholds optimise the performance of the EEG for the diagnosis of hepatic encephalopathy and can be adopted without the need to alter data recording or the initial processing of traces.
authors.affiliation: Department of Neurophysiology, Royal Free Hospital, Royal Free London NHS Foundation Trust, Pond Street, London NW3 2QG, UK. Electronic address: clive.jackson@nhs.net.
authors.forename: Clive D
authors.initials: CD
authors.lastname: Jackson
authors.affiliation: Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark. Electronic address: mg@mech-sense.com.
authors.forename: Mikkel
authors.initials: M
authors.lastname: Gram
authors.affiliation: Department of Neurophysiology, Royal Free Hospital, Royal Free London NHS Foundation Trust, Pond Street, London NW3 2QG, UK. Electronic address: edwin.halliday@gmail.com.
authors.forename: Edwin
authors.initials: E
authors.lastname: Halliday
authors.affiliation: Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark. Electronic address: sso@mech-sense.com.
authors.forename: Søren Schou
authors.initials: SS
authors.lastname: Olesen
authors.affiliation: Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark.
authors.forename: Thomas Holm
authors.initials: TH
authors.lastname: Sandberg
authors.affiliation: Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. Electronic address: amd@rn.dk.
authors.forename: Asbjørn Mohr
authors.initials: AM
authors.lastname: Drewes
authors.affiliation: UCL Institute for Liver and Digestive Health, Department of Medicine, Royal Free Campus, University College London, Hampstead, London NW3 2PF, UK. Electronic address: marsha.morgan@ucl.ac.uk.
authors.forename: Marsha Y
authors.initials: MY
authors.lastname: Morgan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006501:Hepatic Encephalopathy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity
keywords: Diagnostic thresholds; EEG; Hepatic encephalopathy; Psychometry; Spectral analysis; Support vector machine learning