Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Langmuir probe diagnostic studies of pulsed hydrogen plasmas in planar microwave reactors

Langmuir probe techniques have been used to study time and spatially resolved electron densities and electron temperatures in pulse-modulated hydrogen discharges in two different planar microwave reactors (fmicrowave= 2.45 GHz, tpulse= 1 ms). The reactors are (i) a standing-wave radiative slotted waveguide reactor and (ii) a modified travelling-wave radiative slotted waveguide reactor, which generate relatively large plasmas over areas from about 350 cm^2 to 500 cm^2. The plasma properties of these reactor types are of particular interest as they have been used for basic research and for plasma processing, e.g. for surface treatment and layer deposition. In the present study the pressures and microwave powers in the reactors were varied between 33 and 55 Pa and 600 and 3600 W, respectively. In regions with high electromagnetic fields shielded Langmuir probes were used to avoid disturbances of the probe characteristic. Close to the microwave windows of the reactors both the electron density and the electron temperature showed strong inhomogeneities. In the standing-wave reactor the inhomogeneity was found to be spatially modulated by the position of the slots. The maximum value of the electron temperature was about 10 eV and the electron density varied between 0.2 and 14*10^11 cm^-3. The steady state electron temperature in a discharge pulse was reached within a few tens of microseconds whereas the electron density needed some hundreds of microseconds to reach a steady state. Depending on the reactor the electron density reached a maximum between 80 and 200 microseconds after the beginning of the pulse.Comment: 16 pages including 18 figures. The following article has been accepted by J. Appl. Phys. After it is published, it will be found at http://link.aip.or