Relational local electroencephalography representations for sleep scoring.

Computational sleep scoring from multimodal neurophysiological time-series (polysomnography PSG) has achieved impressive clinical success. Models that use only a single electroencephalographic (EEG) channel from PSG have not yet received the same clinical recognition, since they lack Rapid Eye Movement (REM) scoring quality. The question whether this lack can be remedied at all remains an important one. We conjecture that predominant Long Short-Term Memory (LSTM) models do not adequately represent distant REM EEG segments (termed epochs), since LSTMs compress these to a fixed-size vector from separate past and future sequences. To this end, we introduce the EEG representation model ENGELBERT (electroEncephaloGraphic Epoch Local Bidirectional Encoder Representations from Transformer). It jointly attends to multiple EEG epochs from both past and future. Compared to typical token sequences in language, for which attention models have originally been conceived, overnight EEG sequences easily span more than 1000 30 s epochs. Local attention on overlapping windows reduces the critical quadratic computational complexity to linear, enabling versatile sub-one-hour to all-day scoring. ENGELBERT is at least one order of magnitude smaller than established LSTM models and is easy to train from scratch in a single phase. It surpassed state-of-the-art macro F1-scores in 3 single-EEG sleep scoring experiments. REM F1-scores were pushed to at least 86%. ENGELBERT virtually closed the gap to PSG-based methods from 4-5 percentage points (pp) to less than 1 pp F1-score.

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis.

OBJECTIVE: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns. METHODS: We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence. RESULTS: Five sub-cohorts with similar IED activation patterns were found: "Sporadic" (14%, n = 10) without or few IEDs, "Continuous" (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation" (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep" (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal. CONCLUSIONS: Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures. SIGNIFICANCE: This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.

Metabolomics in Sleep, Insomnia and Sleep Apnea.

Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a large-scale manner offers unique opportunities to provide insights into the pathology of diseases by revealing alterations in metabolic pathways. This review aims to summarize the status of metabolomic analyses-based knowledge on sleep disorders and to present knowledge in understanding the metabolic role of sleep in psychiatric disorders. Overall, findings suggest that sleep-wake disorders lead to pronounced alterations in specific metabolic pathways, which might contribute to the association of sleep disorders with other psychiatric disorders and medical conditions. These alterations are mainly related to changes in the metabolism of branched-chain amino acids, as well as glucose and lipid metabolism. In insomnia, alterations in branched-chain amino acid and glucose metabolism were shown among studies. In obstructive sleep apnea, biomarkers related to lipid metabolism seem to be of special importance. Future studies are needed to examine severity, subtypes and treatment of sleep-wake disorders in the context of metabolite levels.