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

Abstract 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 <10th percentile and/or abnormal umbilical artery flow, n = 28) underwent a 24-hour Holter monitoring at the mean age of 9 years and gestational age matched children with birth weight appropriate for gestational age (AGA, n = 19) served as controls. Time- and frequency domain HRV indices were measured and their associations with antenatal hemodynamic changes were analyzed. Results: Time- and frequency domain HRV parameters (standard deviation of R–R intervals, SDNN; low frequency, LF; high frequency, HF; LF/HF; very low frequency, VLF) did not differ significantly between FGR and AGA groups born between 24 and 40 weeks. Neither did they differ between children born with FGR and normal umbilical artery pulsatility or increased umbilical artery pulsatility. In total, 56% of the FGR children demonstrated blood flow redistribution (cerebroplacental ratio, CPR < −2 SD) during fetal life and their SDNN (p = .01), HF (p = .03) and VLF (p = .03) values were significantly lower than in FGR children with CPR ≥ −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

Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG

Abstract 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% (κ = 0.729 ) for five sleep stages (W, N1, N2, N3, and R), 84.1% (κ = 0.773 ) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (κ = 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