The continuous monitoring of partial pressure of blood carbon dioxide (pCO2) in premature babies has proven to be challenging. Spot measurements of pCO2 can be performed by taking a blood sample. However the frequency of such measurements is limited by their invasiveness.
Aim
We aim to develop a continuous non-invasive method of predicting pCO2 using features of the preterm electroencephalography (EEG) signal.
Methods
A regression model was trained on eight 12 hour EEG recordings that contained 22 blood gas measurements in total. All measurements were obtained from babies born before 28 weeks’ gestation and less than 72 hours old. The duration of EEG quiescence (interburst interval) and relative power of delta EEG frequency band values surrounding the point pCO2 measurements were averaged using a specified smoothing window.
Results
It is shown that by combining the measurements of both a defined period of EEG interburst interval and the relative power of delta EEG frequency band using a multivariate linear regression model, a prediction of pCO2 can be performed. The automatic removal of mechanical artefact and artefact due to other external influences is demonstrated. A regression coefficient (R2) of 0.64 is obtainable using both the interburst and delta relative power as predictors for pCO2. All variables are significant to within p<0.05. A section of continuous prediction of pCO2 using EEG showing correlation with simultaneous transcutaneous carbon dioxide measurement is demonstrated.
Conclusion
The ability to provide a novel non-invasive continuous monitoring of pCO2 in newborn preterm babies is discussed.
Continuous monitoring of partial pressure of arterial blood carbon dioxide (PaCO2) is important in preterm babies during the first 36 h after birth to avoid episodes of hypo/hypercarbia. There is a need to develop a reliable technique for the continuous monitoring of PaCO2. The purpose of this study was to determine if continuous monitoring of PaCO2 can be performed through the automatic analysis of preterm electroencephalography (EEG).
Aim
We aimed to determine the levels of agreement between PaCO2 measured by blood gas analysis and the partial pressure of carbon dioxide (PeegCO2) predicted by automatic analysis of EEG.
Methods
Thirty-six hour EEG recordings using 7 hydrogel leads were performed in 12 babies born before 30 weeks’ gestation from soon after birth. Babies with abnormal cranial ultrasound scans were excluded from analysis. Routine arterial blood gases were performed using ABL 835 Flex (Radiometer). EEG was automatically analysed in real-time for changes in amplitude, frequency and interburst intervals in the EEG waveforms. PeegCO2 was continuously predicted using an algorithm. Impedance was measured hourly and accepted if below 5 kOhms. Artefactual EEG was managed automatically by the software and through qualitative reporting.
Results
A strong correlation (R2 = 0.71; n = 49; p < 0.001) between PaCO2 and PeegCO2 was demonstrated. The overall bias (mean difference: PaCO2–PeegCO2) was 0.05 kPa and the precision (standard deviation of differences) was 0.56 kPa.
Conclusion
PeegCO2 monitoring is a new development in the field of continuous monitoring in neonatal intensive care. Bias and precision data indicate that it can be a valuable clinical tool.
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