Nocturnal short-term HRV reflects impaired daytime vigilance better than overnight HRV in suspected OSA patients.

In obstructive sleep apnea (OSA), heart rate variability (HRV) decreases and performance in psychomotor vigilance task (PVT) worsens with more severe hypoxic load. Nevertheless, the association between HRV and PVT performance is poorly understood. Thus, we hypothesize that nocturnal short-term HRV is better related to daytime psychomotor vigilance compared to overnight HRV.

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.

Acute Cardiorespiratory Coupling Impairment in Worsening Sleep Apnea-Related Intermittent Hypoxemia.

Objective: Hypoxic load is one of the main characteristics of obstructive sleep apnea (OSA) contributing to sympathetic overdrive and weakened cardiorespiratory coupling (CRC). Whether this association changes with increasing hypoxic load has remained obscure. Therefore, we aimed to study our hypothesis that increasing hypoxic load acutely decreases the CRC.

Self-applied somnography: technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults.

Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed.

Generalizable Deep Learning-based Sleep Staging Approach for Ambulatory Textile Electrode Headband Recordings.

Reliable, automated, and user-friendly solutions for the identification of sleep stages in home environment are needed in various clinical and scientific research settings. Previously we have shown that signals recorded with an easily applicable textile electrode headband (FocusBand, T 2 Green Pty Ltd) contain characteristics similar to the standard electrooculography (EOG, E1-M2). We hypothesize that the electroencephalographic (EEG) signals recorded using the textile electrode headband are similar enough with standard EOG in order to develop an automatic neural network-based sleep staging method that generalizes from diagnostic polysomnographic (PSG) data to ambulatory sleep recordings of textile electrode-based forehead EEG.