No abstract

Summary Mutations in STXBP1 have been identified in a subset of patients with early onset epileptic encephalopathy (EE), but the full phenotypic spectrum remains to be delineated. Therefore, we screened a cohort of 160 patients with an unexplained EE, including patients with early myoclonic encephalopathy (EME), Ohtahara syndrome, West syndrome, nonsyndromic EE with onset in the first year, and Lennox‐Gastaut syndrome (LGS). We found six de novo mutations in six patients presenting as Ohtahara syndrome (2/6, 33%), West syndrome (1/65, 2%), and nonsyndromic early onset EE (3/64, 5%). No mutations were found in LGS or EME. Only two of four mutation carriers with neonatal seizures had Ohtahara syndrome. Epileptic spasms were present in five of six patients. One patient with normal magnetic resonance imaging (MRI) but focal seizures underwent epilepsy surgery and seizure frequency dropped drastically. Neuropathology showed a focal cortical dysplasia type 1a. There is a need for additional neuropathologic studies to explore whether STXBP1 mutations can lead to structural brain abnormalities.

The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate.

In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test.

The use of well-described databases and input of different experts will improve neonatal EEG interpretation and help to develop uniform seizure definitions, useful for evidence-based studies of seizure recognition and management.