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Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: a population-based study.

ABSTRACT: The yield of epileptiform abnormalities in serial electroencephalography (EEG) studies has not been addressed in a population-based setting for subjects with incident epilepsy or a single unprovoked seizure, raising the possibility of methodologic limitations such as selection bias. Our aim was to address these limitations by assessing the yield and predictors of epileptiform abnormalities for the first and subsequent EEG recording in a study of incident epilepsy or single unprovoked seizure in Rochester, Minnesota.We used the resources of the Rochester Epidemiology Project to identify all 619 residents of Rochester, Minnesota, born in 1920 or later with a diagnosis of incident epilepsy (n = 478) or single unprovoked seizure (n = 141) between 1960 and 1994, who had at least one EEG study. Information on all EEG studies and their results was obtained by comprehensive review of medical records.Among subjects with epilepsy, the cumulative yield of epileptiform abnormalities was 53% after the first EEG study and 72% after the third. Among subjects with a single unprovoked seizure, the cumulative yield was 39% after the first EEG study and 68% after the third. Young age at diagnosis and idiopathic etiology were risk factors for finding epileptiform abnormalities across all EEG recordings.Although the cumulative yield of epileptiform abnormalities increases over successive EEG recordings, there is a decrease in the increment for each additional EEG study after the first EEG study. This is most evident in incident epilepsy and in younger subjects. Clinically it may be worthwhile to consider that the probability of finding an epileptiform abnormality after the third nonepileptiform EEG recording is low.

SUBMITTER: Baldin E

PROVIDER: S-EPMC4167205 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: a population-based study.

Baldin Elisa E Hauser W Allen WA Buchhalter Jeffrey R JR Hesdorffer Dale C DC Ottman Ruth R

Epilepsia 20140709 9

<h4>Objective</h4>The yield of epileptiform abnormalities in serial electroencephalography (EEG) studies has not been addressed in a population-based setting for subjects with incident epilepsy or a single unprovoked seizure, raising the possibility of methodologic limitations such as selection bias. Our aim was to address these limitations by assessing the yield and predictors of epileptiform abnormalities for the first and subsequent EEG recording in a study of incident epilepsy or single unpr ...[more]

PMID: 25041095

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Project description:BACKGROUND:Increasing numbers of reports show the beneficial effects of listening to Mozart music in decreasing epileptiform discharges as well as seizure frequency in epileptic children. There has been no effective method to reduce seizure recurrence after the first unprovoked seizure until now. In this study, we investigated the effect of listening to Mozart K.448 in reducing the seizure recurrence rate in children with first unprovoked seizures. METHODS:Forty-eight children who experienced their first unprovoked seizure with epileptiform discharges were included in the study. They were randomly placed into treatment (n = 24) and control (n = 24) groups. Children in the treatment group listened to Mozart K.448 daily before bedtime for at least six months. Two patients in the treatment group were excluded from analysis due to discontinuation intervention. Finally, forty-six patients were analyzed. Most of these patients (89.1%) were idiopathic in etiology. Seizure recurrence rates and reduction of epileptiform discharges were compared. RESULTS:The average follow-up durations in the treatment and control groups were 18.6 ± 6.6 and 20.1 ± 5.1 months, respectively. The seizure recurrence rate was estimated to be significantly lower in the treatment group than the control group over 24 months (37.2% vs. 76.8%, p = 0.0109). Significant decreases in epileptiform discharges were also observed after 1, 2, and 6 months of listening to Mozart K.448 when compared with EEGs before listening to music. There were no significant differences in gender, mentality, seizure type, and etiology between the recurrence and non-recurrence groups. CONCLUSIONS:Although the case number was limited and control music was not performed in this study, the study revealed that listening to Mozart K.448 reduced the seizure recurrence rate and epileptiform discharges in children with first unprovoked seizures, especially of idiopathic etiology. We believe that Mozart K.448 could be a promising alternative treatment in patients with first unprovoked seizures and abnormal EEGs. Further large-scaled study should be conducted to confirm the effect. TRIAL REGISTRATION:NCT01892605, date: June-19-2013.

Project description:Importance:Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective:To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants:A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13?262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures:SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results:SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance:In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

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Dataset Information

Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: a population-based study.

Publications

Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: a population-based study.

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