Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use

Attenuated Heart Rate Response is Associated with Hypocretin Deficiency in Patients with Narcolepsy

STUDY OBJECTIVE: Several studies have suggested that hypocretin-1 may influence the cerebral control of the cardiovascular system. We analyzed whether hypocretin-1 deficiency in narcolepsy patients may result in a reduced heart rate response. DESIGN: We analyzed the heart rate response during various sleep stages from a 1-night polysomnography in patients with narcolepsy and healthy controls. The narcolepsy group was subdivided by the presence of +/− cataplexy and +/− hypocretin-1 deficiency. SETTING: Sleep laboratory studies conducted from 2001-2011. PARTICIPANTS: In total 67 narcolepsy patients and 22 control subjects were included in the study. Cataplexy was present in 46 patients and hypocretin-1 deficiency in 38 patients. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: All patients with narcolepsy had a significantly reduced heart rate response associated with arousals and leg movements (P < 0.05). Heart rate response associated with arousals was significantly lower in the hypocretin-1 deficiency and cataplexy groups compared with patients with normal hypocretin-1 levels (P < 0.04) and patients without cataplexy (P < 0.04). Only hypocretin-1 deficiency significantly predicted the heart rate response associated with arousals in both REM and non-REM in a multivariate linear regression. CONCLUSIONS: Our results show that autonomic dysfunction is part of the narcoleptic phenotype, and that hypocretin-1 deficiency is the primary predictor of this dysfunction. This finding suggests that the hypocretin system participates in the modulation of cardiovascular function at rest. CITATION: Sorensen GL; Knudsen S; Petersen ER; Kempfner J; Gammeltoft S; Sorensen HBD; Jennum P. Attenuated heart rate response is associated with hypocretin deficiency in patients with narcolepsy. SLEEP 2013;36(1):91–98

Subdural to subgaleal EEG signal transmission: The role of distance, leakage and insulating affectors

ObjectiveTo estimate the area of cortex affecting the extracranial EEG signal. MethodsThe coherence between intra- and extracranial EEG channels were evaluated on at least 10min of spontaneous, awake data from seven patients admitted for epilepsy surgery work up. ResultsCortical electrodes showed significant extracranial coherent signals in an area of approximately 150cm2 although the field of vision was probably only 31cm2 based on spatial averaging of intracranial channels taking into account the influence of the craniotomy and the silastic membrane of intracranial grids. Selecting the best cortical channels, it was possible to increase the coherence values compared to the single intracranial channel with highest coherence. The coherence seemed to increase linearly with an accumulation area up to 31cm2, where 50% of the maximal coherence was obtained accumulating from only 2cm2 (corresponding to one channel), and 75% when accumulating from 16cm2.ConclusionThe skull is an all frequency spatial averager but dominantly high frequency signal attenuator. SignificanceAn empirical assessment of the actual area of cerebral sources generating the extracranial EEG provides better opportunities for clinical electroencephalographers to determine the location of origin of particular patterns in the EEG