Effect of smart air cell mattress on sleep quality / 国际生物医学工程杂志

Objective:To investigate the effect of smart air cell mattresses on sleep quality.Methods:Twenty healthy young people were enrolled as subjects, and each subject underwent a four-night polysomnographic monitoring experiment, including two nights each on a smart air cell mattress and a general mattress. The differences in sleep quality were compared by self-assessment of sleep quality, objective sleep indicators, and electroencephalogram (EEG) spectral analysis.Results:In the comparison between the smart air cell mattress and the general mattress, the differences in self-assessment of sleep quality and objective sleep indicators were not statistically significant (all P > 0.05), but the smart air cell mattress had a slight overall advantage. The relative power of EEG in the low-frequency band and the relative power of EEG in the high-frequency band were higher in the subjects with the smart air cell mattress. Conclusions:For the healthy young population, the smart air cell mattress can positively influence sleep quality to some extent, and the change in EEG relative power indicates an increase in sleep depth.

Automatic artefact detection in single-channel sleep EEG recordings.

Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high-frequency power (25-90 Hz or 45-90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine.

Sleep electroencephalographic characteristics of the Cynomolgus monkey measured by telemetry.

Cynomolgus monkeys are widely used as models of diseases and in pre-clinical studies to assess the impact of new pharmacotherapies on brain function and behaviour. However, the time course of electroencephalographic delta activity during sleep, which represents the main marker of sleep intensity associated with recovery during sleep, has never been described in this non-human primate. In this study, telemetry implants were used to record one spontaneous 24-h sleep-wake cycle in four freely-moving Cynomolgus monkeys, and to quantify the time course of electroencephalographic activity during sleep using spectral analysis. Animals presented a diurnal activity pattern interrupted by short naps. During the dark period, most of the time was spent in sleep with non-rapid eye movement sleep/rapid eye movement sleep alternations and sleep consolidation profiles intermediate between rodents and humans. Deep non-rapid eye movement sleep showed a typical predominance at the beginning of the night with decreased propensity in the course of the night, which was accompanied by a progressive increase in rapid eye movement sleep duration. Spectral profiles showed characteristic changes between vigilance states as reported in other mammalian species. Importantly, delta activity also followed the expected time course of variation, showing a build-up with wakefulness duration and dissipation across the night. Thus, Cynomolgus monkeys present typical characteristics of sleep architecture and spectral structure as those observed in other mammalian species including humans, validating the use of telemetry in this non-human primate model for translational sleep studies.