Nocturnal frontal lobe epilepsy (NFLE) has been delineated as a distinct syndrome in the heterogeneous group of paroxysmal sleep-related disturbances. The variable duration and intensity of the seizures distinguish three non-rapid eye movement-related subtypes: paroxysmal arousals, characterized by brief and sudden recurrent motor paroxysmal behaviour; nocturnal paroxysmal dystonia, motor attacks with complex dystonic-dyskinetic features; and episodic nocturnal wanderings, stereotyped, agitated somnambulism. We review the clinical and polysomnographic data related to 100 consecutive cases of NFLE in order to define the clinical and neurophysiological characteristics of the different seizure types that constitute NFLE. NFLE seizures predominate in males (7:3). Age at onset of the nocturnal seizures varies, but centres during infancy and adolescence. A familial recurrence of the epileptic attacks is found in 25% of the cases, while 39% of the patients present a family history of nocturnal paroxysmal episodes that fit the diagnostic criteria for parasomnias. A minority of cases (13%) have personal antecedents (such as birth anoxia, febrile convulsions) or brain CT or MRI abnormalities (14%). In many patients, ictal (44%) and interictal (51%) EEGs are uninformative. Marked autonomic activation is a common finding during the seizures. NFLE does not show a tendency to spontaneous remission. Carbamazepine completely abolishes the seizures in approximately 20% of the cases and gives remarkable relief (reduction of the seizures by at least 50%) in another 48%. VideoEEG recordings confirm that NFLE comprises a spectrum of distinct phenomena, different in intensity but representing a continuum of the same epileptic condition. We believe that the detailed clinical and videoEEG characterization of patients with NFLE represents the first step towards a better understanding of the pathogenic mechanisms and different clinical outcomes of the various seizure types that constitute the syndrome.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.