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.

Significance: 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.

Keywords: Diagnostic thresholds; EEG; Hepatic encephalopathy; Psychometry; Spectral analysis; Support vector machine learning.