Background Intrauterine growth restriction (IUGR) is a fetal condition characterized by growth-rate reduction. Afflicted fetuses tend to display abnormalities in heart rate. Objective To study the differences in the heart-rate variability of low-risk fetuses and IUGR fetuses during different behavioral states. Methods A total of 40 fetal magnetocardiograms were recorded from 20 low-risk and 20 IUGR fetuses using a 151-sensor SQUID-array system. The maternal cardiac signals were attenuated using signal-space projection. Fetal R waves were identified using an adaptive Hilbert transform approach and fetal heart rate calculated. In each three-minute window, the heart rate was classified into patterns reflective of quiet sleep (pattern A) and active sleep (pattern B) using the criteria of Nijhuis. Two adjacent 3-minute windows exhibiting the same pattern were selected for analysis from every dataset. Heart-rate variability in that 6-minute window was characterized using three measures, Standard Deviation of Normal to Normal (SDNN), Root Mean Square of Successive Differences (RMSSD) and Phase Plane Area (PPA). Results All three measures tended to be lower in the IUGR group compared to the low-risk group. However, when the measures were analyzed in patterns, only PPA showed significant difference between the risk groups in Pattern A, where as both PPA and SDNN showed highly significant risk-group differences in Pattern B. RMSSD did not show any significant risk-group difference. Conclusion The result signifies that the heart-rate variability of IUGR fetuses is different from that of low-risk fetuses, and only PPA was able to capture the HRV differences in both quiet and active states. The difference between these two groups of fetuses shows that the fetal-activity states are potential confounders when characterizing heart-rate variability.

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Using a phase plane analysis (PPA) of the spatial spread of trajectories of the fetal heart rate and its time-derivative we characterize the fetal heart rate patterns (fHRP) as defined by Nijhuis. For this purpose, we collect 22 fetal magnetocardiogram using a 151 SQUID system from 22 low-risk fetuses in gestational ages ranging from 30 to 37 weeks. Each study lasted for 30 minutes. After the attenuation of the maternal cardiac signals, we identify the R waves using an adaptive Hilbert transform approach and calculate the fetal heart rate. On these datasets, we apply the proposed approach and the traditionally used approaches such as standard deviation of the normal to normal intervals (SDNN) and root mean square of the successive difference (RMSSD). Heart rate patterns are scored by an expert using Nijhuis criteria and revealed A, B, and D patterns. A receiver operator characteristic (ROC) curve is used to assess the performance of the metric to differentiate the different patterns. Results showed that only PPA was able to differentiate all pairs of fHRP with high performance.

Introduction/Aim Obstructive sleep apnoea is increasingly prevalent, with shorter referral to treatment time being associated with improved outcomes. Current studies describe a mean wait-time from initial referral to first outpatient review of 88 days, and from first review to diagnostic polysomnography of 123 days. This quality assurance initiative assessed how our sleep disorders centre in an Australian tertiary hospital compared to existing literature, and attempted to verify how triaging affected wait-time. Methods We retrospectively reviewed patients undergoing diagnostic polysomnography from 1st January 2019 to 30th June 2021. Time from initial referral to first clinic review, plus time from initial review to polysomnography, were recorded. Patient demographics and triage category of requested polysomnography were noted. Microsoft Excel was used to collect data and derive descriptive statistics. Results 380 patients (202-male, 178-female) were included. 251 GP referrals were received. 112 patients were triaged for polysomnography within 30 days of initial review (category 4), 204 patients were triaged within 90 days (category 5), and 44 patients were non-urgent (category 6). Mean number of days between initial referral and first review was 136.13 days. Mean number of days between first review and polysomnography was 28.95 days in category 4, 93.38 days in category 5, and 180 days in category 6. Conclusion Time from initial referral to initial review appeared longer in this study compared to published standards. However, time from initial review to polysomnography appeared shorter. Adjusting patient triaging and/or our ability to see new referrals sooner is required to match the published standards.