Febrile infection-related epilepsy syndrome (FIRES) is a devastating epileptic encephalopathy with limited treatment options and unclear etiology. Vagus nerve stimulation (VNS) is an FDA-approved therapy for refractory epilepsy that has been shown to decrease the frequency and severity of seizures. There is a growing interest in alternate non-pharmaceutical therapies for managing super-refractory status epilepticus (SRSE). We present a 29-month-old case, diagnosed with FIRES, whose seizures were successfully controlled by utilization of VNS after ineffective response to intensive pharmacotherapy and ketogenic diet treatment. The VNS was planted after 14 days of refractory seizure activity with a following rapid parameter titration for 42 days without evident side effect, which finally controlled the seizure in the acute phase. VNS may be a potential candidate for the treatment of SRSE in FIRES.
Objective. Developmental and epileptic encephalopathy (DEE) is characterized by refractory seizures, developmental delay or intellectual disability, which may be caused by gene mutation. In this study, we explored the clinical phenotype and long-term outcome in children with genetic early-infantile-onset DEEs. Methods. Next-generation sequencing was performed on 470 patients diagnosed with early-infantile-onset DEE between 2010 and 2020. The genetic variation in all cases was classified and evaluated to identify pathogenic variants. The identified variants were further verified by Sanger sequencing. Results. A total of 118 and 10 patients were found to have putative disease-causing gene mutations and copy number variations, respectively. SCN1A mutations were detected in 38 patients (38/118, 32.2%), representing the largest proportion. In patients with early-infantile-onset DEE with burst suppression, KCNQ2 mutation was found in six patients, and the remaining mutations were reported in SCN2A (n=2) and STXBP1 (n=1). Seven patients with dyskinesia were described. In patients with non-syndromic genetic early-infantile-onset DEEs, we detected possible rare pathogenic variants in SETBP1, DPYD, CSNK2B, and H3F3A. With regards to inheritance pattern, de novo heterozygous mutations accounted for the majority (104/118; 88.1%). Three patients with SMC1A mutations responded well to ketogenic diet add-on therapy. Addition of valproic acid showed good therapeutic effects against KCNB1 and PACS2 encephalopathy. Significance. We detected four possible rare pathogenic gene variants as nonsyndromic genetic causes of early-infantile-onset DEEs. Although early-infantileonset DEEs responded poorly to antiseizure medication treatment, we found that specific antiseizure medications showed good therapeutic effects in some patients with early-infantile-onset DEEs harbouring gene variants.
Objective. Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establish an automatic narcolepsy detection method for MSLT. Approach. We construct a two-phase model on MSLT recordings, where ambiguous sleep staging and sleep transition dynamics make joint efforts to address this issue. In phase 1, we extract representative features from electroencephalogram (EEG) and electrooculogram (EOG) signals. Then, the features are input to an EasyEnsemble classifier for automatic sleep staging. In phase 2, we investigate sleep transition dynamics, including sleep stage transitions and sleep stages, and output likelihood of narcolepsy by virtue of principal component analysis (PCA) and a logistic regression classifier. To demonstrate the proposed framework in clinical application, we conduct experiments on 24 participants from our hospital, considering ten patients with narcolepsy and fourteen patients with MSLT negative. Main results. Applying the two-phase leave-one-subject-out testing scheme, the model reaches an accuracy, sensitivity, and specificity of 87.5%, 80.0%, and 92.9% for narcolepsy detection. Influenced by disease pathology, accuracy of automatic sleep staging in narcolepsy appears to decrease compared to that in the non-narcoleptic population. Significance. This method can automatically and efficiently distinguish patients with narcolepsy based on MSLT. It probes into the amalgamation of automatic sleep staging and sleep transition dynamics for narcolepsy detection, which would assist clinic and neuroelectrophysiology specialists in visual interpretation and diagnosis.
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