Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy

Abstract

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).

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“…Additionally, we are looking to test the algorithm with data obtained from humans and other animal models, but there is recent work using similar approaches that lead us to believe our method can be effective in other models. 55,56 Indeed, a recent study demonstrated that another supervised learning algorithm, deep convolutional neural networks, can be used to predict sleep stages from manually scored data in narcoleptic mice with a comparable degree of success as our own approach. 57 An additional value of this work is the characterization of the features we use to train our model and score recordings.…”

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“…One study has even shown that two-channel subscalp EEG is sufficient to do robust sleep staging. 71,72 | 1813 DUUN-HENRIKSEN Et al.…”

“…Furthermore, as a relationship between sleep quality/duration and seizure risk has been suggested, the ability to record objective sleep quality and seizures is critical to understanding whether strategies to improve sleep can help seizure control. One study has even shown that two‐channel subscalp EEG is sufficient to do robust sleep staging 71,72 …”