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ORIGINAL ARTICLE
Year : 2022  |  Volume : 39  |  Issue : 1  |  Page : 21-27

The Electroencephalographic Evolution of Electrical Status: Is it Possible to Diagnosis ESES from 180 Seconds of Sleep?


Department of Pediatric Neurology, Faculty of Medicine, Gazi University, Ankara, Turkey

Correspondence Address:
Habibe Koc Ucar
Department of Pediatric Neurology, Faculty of Medicine, Gazi University, 06560 Yenimahalle, Ankara
Turkey
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/nsn.nsn_136_21

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Purpose: Electrical status epilepticus during slow sleep (ESES) is an electroclinical syndrome with a specific electroencephalogram (EEG) pattern characterized by epileptic seizures, cognitive decline, and behavioral problems. The EEG pattern is defined by the percentage of the spike-wave index (SWI) in nonrapid eye movement (NREM) sleep without a clear cut-off value. The purpose of this study is to determine the significance of SWI calculation in the first 180 s of the NREM sleep stage. Methods: Patients with tonic seizures and those with SWI levels of <50% were excluded from the study. One hundred patients were enrolled in the study (typical ESES: 85; atypical ESES: 15). EEG findings were evaluated according to the following points: 1-ESES type: atypical ESES for SWI between 50% and 85% or typical ESES for ≥85%; 2-SWI calculation methods: Short method and long conventional method; 3-SWI percentage and spike frequency (SF). Results: A moderate correlation was determined between spike-wave percentage (SWP) and SF (r = 0.628; P < 0.001). A strong positive correlation was determined between the short method and long conventional method (r = 0.888; P < 0.001). In multivariate logistic regression with the SWI short method and the number of spikes in the first 180 s of NREM, only the SWI short method was found to predict typical ESES regardless of other factors (odds ratio: 1.18; P = 0.001). The optimal predictive value of the SWI short method for predicting typical ESES was >85, with sensitivity of 81.2%, and specificity of 73.3% (+PV: 94.5%, −PV: 40.7%; AUC ± SE = 0.850 ± 0.05; P < 0.001). Conclusion: Evaluating EEG epileptiform activities with objective and reproducible well-defined measurements such as SWP and SF allows for the comparison of different patient groups. We think that a shorter method for diagnosing ESES would potentially provide increased cost savings and patient comfort.


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