Antenatal hemodynamic findings and heart rate variability in early school-age children born with fetal growth restriction

Background: According to epidemiological studies, impaired intrauterine growth increases the risk for cardiovascular morbidity and mortality in adulthood. Heart rate variability (HRV), which reflects the autonomic nervous system function, has been used for risk assessment in adults while its dysfunction has been linked to poor cardiovascular outcome. Objective: We hypothesized that children who were born with fetal growth restriction (FGR) and antenatal blood flow redistribution have decreased HRV at early school age compared to their gestational age matched peers with normal intrauterine growth. Study design: A prospectively collected cohort of children born with FGR (birth weight = -2SD. Conclusions: Early school age children born with FGR and intrauterine blood flow redistribution demonstrated altered heart rate variability. These prenatal and postnatal findings may be helpful in targeting preventive cardiovascular measures in FGR.Peer reviewe

Towards a Deeper Understanding of Sleep Stages through their Representation in the Latent Space of Variational Autoencoders

Artificial neural networks show great success in sleep stage classification, with an accuracy comparable to human scoring. While their ability to learn from labelled electroencephalography (EEG) signals is widely researched, the underlying learning processes remain unexplored. Variational autoencoders can capture the underlying meaning of data by encoding it into a low-dimensional space. Regularizing this space furthermore enables the generation of realistic representations of data from latent space samples. We aimed to show that this model is able to generate realistic sleep EEG. In addition, the generated sequences from different areas of the latent space are shown to have inherent meaning. The current results show the potential of variational autoencoders in understanding sleep EEG data from the perspective of unsupervised machine learning

Tulevaisuudennäkymiä

Non peer reviewe

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in-home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re-trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% (kappa = 0.729) for five sleep stages (W, N1, N2, N3, and R), 84.1% (kappa = 0.773) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (kappa = 0.801) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in-home PSGs.Peer reviewe

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

Funding Information: Financial support for this study was provided by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No 965417, by the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (Grants 5041807, 5041804, 5041803, 5041797, and 5041794), by the Finnish Cultural Foundation through Kainuu Regional Fund and Central fund, by Olvi Foundation, by the Finnish Anti‐Tuberculosis Association, by Tampere Tuberculosis Foundation, by the Research Foundation of the Pulmonary Diseases, by the NordForsk (NordSleep Project 90458) through the Business Finland (Grant 5133/31/2018), by the Kuopio University Hospital Research Foundation, and The Icelandic Centre for Research. Publisher Copyright: © 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.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. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin–electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%–43.9%, p < 0.001) and less absolute power (at 1–40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3–1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.Peer reviewe

Increased nocturnal arterial pulsation frequencies of obstructive sleep apnoea patients is associated with an increased number of lapses in a psychomotor vigilance task.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadObjectives: Besides hypoxaemia severity, heart rate variability has been linked to cognitive decline in obstructive sleep apnoea (OSA) patients. Thus, our aim was to examine whether the frequency domain features of a nocturnal photoplethysmogram (PPG) can be linked to poor performance in the psychomotor vigilance task (PVT). Methods: PPG signals from 567 suspected OSA patients, extracted from Type 1 diagnostic polysomnography, and corresponding results of PVT were retrospectively examined. The frequency content of complete PPGs was determined, and analyses were conducted separately for men (n=327) and women (n=240). Patients were grouped into PVT performance quartiles based on the number of lapses (reaction times ≥500 ms) and within-test variation in reaction times. The best-performing (Q1) and worst-performing (Q4) quartiles were compared due the lack of clinical thresholds in PVT. Results: We found that the increase in arterial pulsation frequency (APF) in both men and women was associated with a higher number of lapses. Higher APF was also associated with higher within-test variation in men, but not in women. Median APF (β=0.27, p=0.01), time spent under 90% saturation (β=0.05, p<0.01), female sex (β=1.29, p<0.01), older age (β=0.03, p<0.01) and subjective sleepiness (β=0.07, p<0.01) were significant predictors of belonging to Q4 based on lapses. Only female sex (β=0.75, p<0.01) and depression (β=0.91, p<0.02) were significant predictors of belonging to Q4 based on the within-test variation. Conclusions: In conclusion, increased APF in PPG provides a possible polysomnography indicator for deteriorated vigilance especially in male OSA patients. This finding highlights the connection between cardiorespiratory regulation, vigilance and OSA. However, our results indicate substantial sex-dependent differences that warrant further prospective studies.Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding Academy of Finland Seinajoki Central Hospital Competitive State Research Financing of Expert Responsibility Area of Tampere University Hospital VTR3242 Business Finland Paulo Foundation Paivikki & Sakari Sohlberg Foundation Research Foundation of the Pulmonary Diseases Finnish Cultural Foundation Alfred Kordelin Foundation Tampere Tuberculosis Foundation Respiratory Foundation of Kuopio Regio

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

Publisher Copyright: © 2013 IEEE.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. Standard EOG signals together with manually annotated sleep stages from clinical PSG dataset (n = 876) were used to train, validate, and test a fully convolutional neural network (CNN). Furthermore, ambulatory sleep recordings including a standard set of gel-based electrodes and the textile electrode headband were conducted for 10 healthy volunteers at their homes to test the generalizability of the model. In the test set (n = 88) of the clinical dataset, the model's accuracy for 5-stage sleep stage classification was 80% (κ = 0.73) using only the single-channel EOG. The model generalized well for the headband-data, reaching 82% (κ = 0.75) overall sleep staging accuracy. In comparison, accuracy of the model was 87% (κ = 0.82) in home recordings using the standard EOG. In conclusion, the CNN model shows potential on automatic sleep staging of healthy individuals using a reusable electrode headband in a home environment.Peer reviewe