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.
- Nonconvulsive status epilepticus
- Nonlinear dynamical measures
- Toxic/metabolic encephalopathy
ASJC Scopus subject areas
- Clinical Neurology