PMID- 19732132
OWN - NLM
STAT- MEDLINE
DCOM- 20100415
LR  - 20100324
IS  - 1528-1167 (Electronic)
IS  - 0013-9580 (Linking)
VI  - 51
IP  - 2
DP  - 2010 Feb
TI  - Real-time differentiation of nonconvulsive status epilepticus from other 
      encephalopathies using quantitative EEG analysis: a pilot study.
PG  - 243-50
LID - 10.1111/j.1528-1167.2009.02286.x [doi]
AB  - PURPOSE: Distinguishing nonconvulsive status epilepticus (NCSE) from some 
      nonepileptic encephalopathies is a challenging problem. In many situations, NCSE 
      and nonepileptic encephalopathies are indistinguishable by clinical symptoms and 
      can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or 
      delay to diagnosis of NCSE may increase the rate of morbidity and mortality. 
      METHODS: We developed a fast-differentiating algorithm using quantitative EEG 
      analysis to distinguish NCSE patients from patients with toxic/metabolic 
      encephalopathy (TME). EEG recordings were collected from 11 patients, including 6 
      with NCSE and 5 with TME. Three nonlinear dynamic measures were used in the 
      proposed algorithm: the maximum short-term Lyapunov exponent (STLmax), phase of 
      attractor (phase/angular frequency), and approximate entropy (ApEn). A further 
      refined metric derived from STLmax and phase of attractor (the mean distance to 
      EEG epoch samples from their centroid in the feature space) was also utilized as 
      a criterion. Paired t tests were carried out to further clarify the separation 
      between the EEG patterns of NCSE and TME. RESULTS: Computational results showed 
      that the performance of the proposed algorithm was sufficient to distinguish NCSE 
      from TME. The results were consistent in all subjects in our study. CONCLUSIONS: 
      The study presents evidence that the maximum short-term Lyapunov exponents 
      (STLmax) and phase of attractors (phase/angular frequency) can be useful in 
      assisting clinical diagnosis of NCSE. Findings presented in this article provide 
      a promising indication that the proposed algorithm may correctly distinguish NCSE 
      from TME. Although the exact mechanism of this association remains unknown, the 
      authors suggest that epileptic activity is highly associated with and can be 
      modeled by dynamic systems.
FAU - Zhang, Jicong
AU  - Zhang J
AD  - Department of Industrial and Systems Engineering, University of Florida, 
      Gainesville, Florida, USA.
FAU - Xanthopoulos, Petros
AU  - Xanthopoulos P
FAU - Liu, Chang-Chia
AU  - Liu CC
FAU - Bearden, Scott
AU  - Bearden S
FAU - Uthman, Basim M
AU  - Uthman BM
FAU - Pardalos, Panos M
AU  - Pardalos PM
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20090903
PL  - United States
TA  - Epilepsia
JT  - Epilepsia
JID - 2983306R
SB  - IM
MH  - Adult
MH  - Aged
MH  - Algorithms
MH  - Brain Diseases, Metabolic/diagnosis
MH  - Diagnosis, Differential
MH  - Diagnostic Errors
MH  - Electroencephalography/methods/*statistics & numerical data
MH  - Entropy
MH  - Female
MH  - Humans
MH  - Male
MH  - Nonlinear Dynamics
MH  - Pilot Projects
MH  - Status Epilepticus/classification/*diagnosis
EDAT- 2009/09/08 06:00
MHDA- 2010/04/16 06:00
CRDT- 2009/09/08 06:00
PHST- 2009/09/08 06:00 [entrez]
PHST- 2009/09/08 06:00 [pubmed]
PHST- 2010/04/16 06:00 [medline]
AID - EPI2286 [pii]
AID - 10.1111/j.1528-1167.2009.02286.x [doi]
PST - ppublish
SO  - Epilepsia. 2010 Feb;51(2):243-50. doi: 10.1111/j.1528-1167.2009.02286.x. Epub 
      2009 Sep 3.