2023
DOI: 10.1212/cpj.0000000000200160
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Diagnostic Accuracy of Ambulatory EEG vs Routine EEG in Patients With First Single Unprovoked Seizure

Abstract: Background and ObjectiveTo evaluate the diagnostic accuracy of the ambulatory EEG (aEEG) at detecting interictal epileptiform discharges (IEDs)/seizures compared with routine EEG (rEEG) and repetitive/second rEEG in patients with a first single unprovoked seizure (FSUS). We also evaluated the association between IED/seizures on aEEG and seizure recurrence within 1 year of follow-up.MethodsWe prospectively evaluated 100 consecutive patients with FSUS at the provincial Single Seizure Clinic. They underwent 3 seq… Show more

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Cited by 11 publications
(6 citation statements)
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“…Because of its relatively low signal-to-noise ratio, EEG data is subject to high variability induced by the recording setting, apparel, and even patient-related characteristics (e.g., hair, muscle activation, eye movements). [101] , [102] , [103] In future studies, large-scale initiatives integrating rEEG recordings from multiple centers along with a more widespread use of ambulatory EEG as a diagnostic tool in patients with first unprovoked seizures [104] will likely amplify this challenge. Automated methods for artifact detection and rejection based on deep neural networks are promising alternatives to manual identification, [105] , [106] , [107] but their capacity to increase downstream performances remains unclear.…”
Section: Discussionmentioning
confidence: 99%
“…Because of its relatively low signal-to-noise ratio, EEG data is subject to high variability induced by the recording setting, apparel, and even patient-related characteristics (e.g., hair, muscle activation, eye movements). [101] , [102] , [103] In future studies, large-scale initiatives integrating rEEG recordings from multiple centers along with a more widespread use of ambulatory EEG as a diagnostic tool in patients with first unprovoked seizures [104] will likely amplify this challenge. Automated methods for artifact detection and rejection based on deep neural networks are promising alternatives to manual identification, [105] , [106] , [107] but their capacity to increase downstream performances remains unclear.…”
Section: Discussionmentioning
confidence: 99%
“…Diagnostic accuracy is higher with longer EEG monitoring, 62 although prolonged ambulatory monitoring is limited with conventional EEG equipment. The REMI remote EEG monitoring system by Epitel uses four, single‐patient, single‐use wireless sensors that transmit a single‐channel EEG to a mobile computing device and uses automated seizure detection with machine learning to tag events for clinician review.…”
Section: Seizure Diaries and Detection Devicesmentioning
confidence: 99%
“…1 The true risk is what happens in actuality, and the aEEG seems more accurate in predicting the true risk. 4 It is important to note that ASM treatment does not change the long-term risk of second unprovoked seizure and epilepsy. 1 An interesting finding in the study cohort was that older adults (>60 years) were more likely to have recurrent seizures.…”
Section: Commentarymentioning
confidence: 99%
“…In this vacuum, the study by Hernandez-Ronquillo et al lands with a thunderous bang in favor of ambulatory EEG. 4 This elegant study performed prospectively over 4 years aimed to evaluate the yield of aEEG compared to a first and a repeat rEEG for detecting IEDs/seizures after FSUS. The study cohort had ages between 17 and 82 years, but racial distribution was not disclosed.…”
Section: Commentarymentioning
confidence: 99%
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