Double-sided asymmetric method for automated fetal heart rate baseline calculation

• Published in: Australasian physical & engineering sciences in medicine Vol. 46; no. 4; pp. 1779 - 1790
• Main Authors: Shapira, Rotem, Kedar, Reuven, Yaniv, Yael, Keidar, Noam
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Annotations; Asymmetry; Automation; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Clinical decision making; Decision making; Errors; Female; Fetus - diagnostic imaging; Fetuses; Heart rate; Heart Rate, Fetal - physiology; Humans; Interpolation; Labor, Obstetric - physiology; Least squares; Medical and Radiation Physics; Missing data; Observer Variation; Pregnancy; Scientific Paper; Smoothing; Well being; Signal processing; Electronic fetal monitoring; Obstetric decision making; Gap interpolation; Fetal heart rate; Baseline
• ISSN: 2662-4729; 0158-9938; 2662-4737; 2662-4737; 1879-5447
• DOI: 10.1007/s13246-023-01337-1

The fetal heart rate (FHR) signal is used to assess the well-being of a fetus during labor. Manual interpretation of the FHR is subject to high inter- and intra-observer variability, leading to inconsistent clinical decision-making. The baseline of the FHR signal is crucial for its interpretation. An automated method for baseline determination may reduce interpretation variability. Based on this claim, we present the Auto-Regressed Double-Sided Improved Asymmetric Least Squares (ARDSIAsLS) method as a baseline calculation algorithm designed to imitate expert obstetrician baseline determination. As the FHR signal is prone to a high rate of missing data, a step of gap interpolation in a physiological manner was implemented in the algorithm. The baseline of the interpolated signal was determined using a weighted algorithm of two improved asymmetric least squares smoothing models and an improved symmetric least squares smoothing model. The algorithm was validated against a ground truth determined from annotations of six expert obstetricians. FHR baseline calculation performance of the ARDSIAsLS method yielded a mean absolute error of 2.54 bpm, a max absolute error of 5.22 bpm, and a root mean square error of 2.89 bpm. In a comparison between the algorithm and 11 previously published methods, the algorithm outperformed them all. Notably, the algorithm was non-inferior to expert annotations. Automating the baseline FHR determination process may help reduce practitioner discordance and aid decision-making in the delivery room.