Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis

A pilot study

Jicong Zhang, Petros Xanthopoulos, Chang Chia Liu, Scott Bearden, Basim M. Uthman, Panos M. Pardalos

Research output: Contribution to journalArticle

Abstract

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 ofNCSEmayincrease the rate ofmorbidity and mortality. Methods:Wedeveloped 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 suffi-cient 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 (STL-max) 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.

Original languageEnglish (US)
Pages (from-to)243-250
Number of pages8
JournalEpilepsia
Volume51
Issue number2
DOIs
StatePublished - 2010

Fingerprint

Status Epilepticus
Brain Diseases
Neurotoxicity Syndromes
Metabolic Brain Diseases
Electroencephalography
Nonlinear Dynamics
Entropy
Diagnostic Errors
Mortality

Keywords

  • EEG
  • Epilepsy
  • Nonconvulsive status epilepticus
  • Nonlinear dynamical measures
  • Toxic/metabolic encephalopathy

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology

Cite this

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis : A pilot study. / Zhang, Jicong; Xanthopoulos, Petros; Liu, Chang Chia; Bearden, Scott; Uthman, Basim M.; Pardalos, Panos M.

In: Epilepsia, Vol. 51, No. 2, 2010, p. 243-250.

Research output: Contribution to journalArticle

Zhang, Jicong ; Xanthopoulos, Petros ; Liu, Chang Chia ; Bearden, Scott ; Uthman, Basim M. ; Pardalos, Panos M. / Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis : A pilot study. In: Epilepsia. 2010 ; Vol. 51, No. 2. pp. 243-250.
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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 ofNCSEmayincrease the rate ofmorbidity and mortality. Methods:Wedeveloped 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 suffi-cient 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 (STL-max) 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.

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