We compared three-channel montage with 21-channel neonatal minimal placement montage for the detection of neonatal seizures and quantification of seizure burden. Thirty-five neonatal ...electroencephalograms were retrospectively and blindly reviewed by two independent readers. Tracings were analyzed in the three-channel montage for seizure number, duration, and quantification of seizure burden before reanalysis with the full 21-channel neonatal minimal placement montage. Seizures were identified using standard definitions of electroencephalographic seizure. Sensitivity, specificity, and interrater reliability were calculated. The sensitivity and specificity of three-channel montage for detecting seizures >10 seconds were 91% and 100% for reader 1, respectively, and 82% and 96% for reader 2, respectively. The interrater agreement for detection of seizures was excellent (κ = 0.86, 94% percent overall agreement). For quantification of seizure burden, strong, positive correlation existed between assessments by full montage and three-channel montage (for reader 1, r = 0.945, n = 11, P < 0.0001; for reader 2, r = 0.902, n = 11, P < 0.0001), and strong correlation existed between the readers for three-channel montage ( r = 0.879, n = 11, P < 0.0001). Despite its limitations, three-channel montage is useful in the detection of neonatal seizures and quantification of seizure burden.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK
IFIH1 gain‐of‐function has been reported as a cause of a type I interferonopathy encompassing a spectrum of autoinflammatory phenotypes including Aicardi–Goutières syndrome and Singleton Merten ...syndrome. Ascertaining patients through a European and North American collaboration, we set out to describe the molecular, clinical and interferon status of a cohort of individuals with pathogenic heterozygous mutations in IFIH1. We identified 74 individuals from 51 families segregating a total of 27 likely pathogenic mutations in IFIH1. Ten adult individuals, 13.5% of all mutation carriers, were clinically asymptomatic (with seven of these aged over 50 years). All mutations were associated with enhanced type I interferon signaling, including six variants (22%) which were predicted as benign according to multiple in silico pathogenicity programs. The identified mutations cluster close to the ATP binding region of the protein. These data confirm variable expression and nonpenetrance as important characteristics of the IFIH1 genotype, a consistent association with enhanced type I interferon signaling, and a common mutational mechanism involving increased RNA binding affinity or decreased efficiency of ATP hydrolysis and filament disassembly rate.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Existing automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG ...because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.
The authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactual movement of the neonate or people near the neonate though dense optical flow estimation.
Using the methods mentioned above, 197 periods of patting activity were identified and quantified, of which 45 generated false-positive automated seizure detection events. A binary patting detection algorithm was trained with a subset of 470 event videos. This supervised detection algorithm was applied to a testing subset of 187 event videos with 8 false-positive events, which resulted in a 24% reduction in false-positive automated seizure detections and a 50% reduction in false-positive events caused by neonatal care patting, while maintaining 11 of 12 true-positive seizure detection events.
This work presents a novel approach to improving automated seizure detection algorithms used during neonatal video EEG monitoring. This artifact detection mechanism can improve the ability of a seizure detector algorithm to distinguish between artifact and true seizure activity.
Abstract We prospectively analyzed EEGs from participants in the ongoing NIH Rare Diseases Clinical Research Network Angelman Syndrome Natural History Study. Of the one-hundred-sixty enrolled ...patients (2006–2010), 115 had complete data (58 boys, median age 3.6 years). Distinct EEG findings were intermittent rhythmic delta waves (83.5%), interictal epileptiform discharges (74.2%), intermittent rhythmic theta waves (43.5%), and posterior rhythm slowing (43.5%). Centro-occipital and centro-temporal delta waves decreased with age (p = 0.01, p = 0.03). There were no specific correlations between EEG patterns and genotypes. A classification tree allowed the prediction of deletions class-1 (5.9 Mb) in patients with intermittent theta waves in < 50% of EEG and interictal epileptiform abnormalities; UPD , UBE3A mutation or imprinting defects in patients with intermittent theta in < 50% of EEG without interictal epileptiform abnormalities; deletions class-2 (5.0 Mb) in patients with > 50% theta and normal posterior rhythm; atypical deletions in patients with > 50% theta but abnormal posterior rhythm. EEG patterns are important biomarkers in Angelman syndrome and may suggest the underlying genetic etiology.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK
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