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Ear-EEG contains enough GTCS-specific EMG activity for GTCS detection to be feasible. Ear-EEG could be considered for nocturnal GTCS monitoring as a supplement to SUDEP preventive interventions.

Background The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. A novel subcutaneous device was developed to record ultralong-term EEG, and has been shown to measure events of clinical relevance for patients with epilepsy. We investigated whether subcutaneous EEG recordings can also be used to automatically assess the sleep architecture of epilepsy patients.Method Four adult inpatients with probable or definite temporal lobe epilepsy were monitored simultaneously with long-term video scalp EEG (LTV EEG) and subcutaneous EEG. In total, 11 nights with concurrent recordings were obtained. The sleep EEG in the two modalities was scored independently by a trained expert according to the American Academy of Sleep Medicine (AASM) rules. By using the sleep stage labels from the LTV EEG as ground truth, an automatic sleep stage classifier based on 30 descriptive features computed from the subcutaneous EEG was trained and tested.Results An average Cohen’s kappa of was achieved using patient specific leave-one-night-out cross validation. When merging all sleep stages into a single class and thereby evaluating an awake–sleep classifier, we achieved a sensitivity of 94.8% and a specificity of 96.6%. Compared to manually labeled video-EEG, the model underestimated total sleep time and sleep efficiency by 8.6 and 1.8 min, respectively, and overestimated wakefulness after sleep onset by 13.6 min.Conclusion This proof-of-concept study shows that it is possible to automatically sleep score patients with epilepsy based on two-channel subcutaneous EEG. The results are comparable with the methods currently used in clinical practice. In contrast to comparable studies with wearable EEG devices, several nights were recorded per patient, allowing for the training of patient specific algorithms that can account for the individual brain dynamics of each patient. Clinical trial registered at ClinicalTrial.gov on 19 October 2016 (ID:NCT02946151).

The interaction between sleep and disease is often complex. In epilepsy, seizure risk is modulated by sleep pressure and sleep stage. At the same time, sleep can be affected by both seizures and anti-epileptic drugs (Bazil, 2017; Derry & Duncan, 2013). Polysomnography (PSG) recordings, the laboratory-recorded gold-standard, pose several disadvantages for clinical use. PSG is expensive, time-consuming and requires a trained technician. Furthermore, the individual will normally be required to stay in the laboratory/hospital, which often limits the number of monitoring nights to one or two. Reliable sleep monitoring that can be conducted in the individuals' home environment for longer periods of time is therefore desirable (Vojkan, Grundlehner, Vullers, & Penders, 2015). Although a full PSG montage is sometimes required for diagnosing primary sleep disorders, a smaller-scale system focusing on electroencephalogram (EEG) might be sufficient when we are not diagnosing primary sleep disorders but rather seek to use sleep monitoring as a tool for optimizing treatment in individuals with sleep disturbances, such as those found in epilepsy. In the past decade, several types of mobile EEG systems for sleep measurements have been presented. Some of these sys