Intrapartum hypoxia and power spectral analysis of fetal heart rate variability

Reliable detection of intrapartum fetal acidosis is crucial for preventing morbidity. Hypoxia-related changes of fetal heart rate variability (FHRV) are controlled by the autonomic nervous system. Subtle changes in FHRV that cannot be identified by inspection can be detected and quantified by power spectral analysis. Sympathetic activity relates to low-frequency FHRV and parasympathetic activity to both low- and high-frequency FHRV. The aim was to study whether intra partum fetal acidosis can be detected by analyzing spectral powers of FHRV, and whether spectral powers associate with hypoxia-induced changes in the fetal electrocardiogram and with the pH of fetal blood samples taken intrapartum. The FHRV of 817 R-R interval recordings, collected as a part of European multicenter studies, were analyzed. Acidosis was defined as cord pH ≤ 7.05 or scalp pH ≤ 7.20, and metabolic acidosis as cord pH ≤ 7.05 and base deficit ≥ 12 mmol/l. Intrapartum hypoxia increased the spectral powers of FHRV. As fetal acidosis deepened, FHRV decreased: fetuses with significant birth acidosis had, after an initial increase, a drop in spectral powers near delivery, suggesting a breakdown of fetal compensation. Furthermore, a change in excess of 30% of the low-to-high frequency ratio of FHRV was associated with fetal metabolic acidosis. The results suggest that a decrease in the spectral powers of FHRV signals concern for fetal wellbeing. A single measure alone cannot be used to reveal fetal hypoxia since the spectral powers vary widely intra-individually. With technical developments, continuous assessment of intra-individual changes in spectral powers of FHRV might aid in the detection of fetal compromise due to hypoxia.Siirretty Doriast

Survey on Cardiotocography Feature Extraction Algorithms for Foetal Welfare Assessment

The original version of this chapter was inadvertently published with an incorrect chapter pagination 1187–1192 and DOI 10.1007/978-3-319-32703-7_230. The page range and the DOI has been re-assigned. The correct page range is 1193–1198 and the DOI is 10.1007/978-3-319-32703-7_231. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260 An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260 An erratum to this chapter can be found at https://doi.org/10.1007/978-3-319-32703-7_260Since its inception forty years ago as a way to control birth process, the cardiotocograph (CTG) has emerged over time and became the undisputed leader worldwide of non-invasive intrapartum foetal monitoring systems. The CTG signals conveying a lot of information, it is very difficult to interpret them and act accordingly even for specialists; hence, researchers have started looking for characteristics which could be correlated with a particular pathological state of the foetus. Thereby, many features appeared in the literature, ranging from the most common ones to artificially generated features, and computed using a wide variety of signal processing-based analysis tools: time scale, spectral or non-linear analysis, to name but a few. This survey paper, presents in a hierarchical order the most common processing steps of a CTG signal and focuses primarily on the feature extraction methods for foetal heart rate (FHR) analysis reported in the literature during the last decade. Also, some feature classification methods are reported before a brief discussion which concludes this work