Literature DB >> 34184990

Sensitivity of long-term EEG monitoring as a second diagnostic step in the initial diagnosis of epilepsy.

Roland Renzel1, Lucas Tschaler1, Ian Mothersill2, Lukas L Imbach1, Rosita Poryazova1.   

Abstract

In this retrospective study, we aimed to evaluate the sensitivity and negative predictive value of long-term EEG (L-EEG) in patients being assessed for epilepsy, who had already undergone non-specific standard EEG(s) (S-EEG). Secondary endpoints of this study were: (1) the correlation of non-specific changes on EEG with epileptiform patterns on L-EEG; and (2) the correlation of clinical parameters such as subjective frequency of seizures or epileptogenic lesions on cerebral imaging with epileptiform changes on L-EEG. We retrospectively analysed clinical and electrophysiological data of 75 patients, assessed for epilepsy at the University Hospital Zurich, who had undergone an L-EEG for at least 48 hours, between 2010 and 2015. All patients had already undergone S-EEG(s) before L-EEG, which showed no epileptic changes. Furthermore, the association with clinical parameters, such as frequency of presumptive seizures, abnormalities on standard-EEG, AED intake and cerebral imaging with the final diagnosis, was analysed. Out of 75 patients, 14 (19%) patients were finally diagnosed with epilepsy. In eight of these patients, L-EEGs showed typical ictal/interictal patterns, with a sensitivity of 57% and negative predictive value of 91%. Neither the subjective frequency of seizures nor potentially epileptogenic lesions on cerebral imaging were associated with a positive epilepsy diagnosis. In this preselected cohort of patients, who had already undergone a non-diagnostic S-EEG, the sensitivity of L-EEG remained considerable. Nonetheless, our study also revealed a significant false-negative rate. Based on the high negative predictive value in this study, L-EEG appears to be most useful at excluding epilepsy. Nevertheless, thorough evaluation of seizure history and clinical findings remain crucial for a reliable diagnosis.

Entities:  

Keywords:  EEG; epilepsy; long-term EEG; negative predictive value; sensitivity

Mesh:

Year:  2021        PMID: 34184990     DOI: 10.1684/epd.2021.1298

Source DB:  PubMed          Journal:  Epileptic Disord        ISSN: 1294-9361            Impact factor:   1.819


  1 in total

1.  A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy.

Authors:  Tahereh Najafi; Rosmina Jaafar; Rabani Remli; Wan Asyraf Wan Zaidi
Journal:  Sensors (Basel)       Date:  2022-09-25       Impact factor: 3.847

  1 in total

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