title: Hypoglycemia-related electroencephalogram changes assessed by multiscale entropy.
abstract: BACKGROUND
  Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered.
  
  
  METHODS
  EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic-euglycemic-hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia.
  
  
  RESULTS
  For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales.
  
  
  CONCLUSIONS
  A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning.
authors.affiliation: 1 Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Chiara
authors.initials: C
authors.lastname: Fabris
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.forename: Anne-Sophie
authors.initials: AS
authors.lastname: Sejling
authors.forename: Anahita
authors.initials: A
authors.lastname: Goljahani
authors.forename: Jonas
authors.initials: J
authors.lastname: Duun-Henriksen
authors.forename: Line Sofie
authors.initials: LS
authors.lastname: Remvig
authors.forename: Claus Bogh
authors.initials: CB
authors.lastname: Juhl
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D058256:Brain Waves; D003072:Cognition Disorders / Q000139:chemically induced / Q000209:etiology / Q000503:physiopathology*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000150:complications / Q000503:physiopathology*; D004569:Electroencephalography; D019277:Entropy; D015309:Glucose Clamp Technique; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000503:physiopathology*; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000009:adverse effects
chemical_list: D007004:Hypoglycemic Agents