Laboratory of Bioenergetic and Bioelectric Systems, Biomedical Engineering Faculty, Technion-IIT, Haifa, Israel.
Department of Obstetrics & Gynecology, Carmel Medical Center, Haifa, Israel.
Phys Eng Sci Med. 2023 Dec;46(4):1779-1790. doi: 10.1007/s13246-023-01337-1. Epub 2023 Sep 28.
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.
胎儿心率（FHR）信号用于评估分娩过程中胎儿的健康状况。FHR 的手动解释受到观察者间和观察者内变异性的影响较大，导致临床决策不一致。FHR 信号的基线对于其解释至关重要。基线的自动确定方法可以减少解释的变异性。基于这一说法，我们提出了自回归双边改进不对称最小二乘（ARDSIAsLS）方法，作为一种基线计算算法，旨在模仿专家产科医生的基线确定。由于 FHR 信号容易出现高数据缺失率，因此在算法中实现了以生理方式进行间隙插值的步骤。通过两个改进的不对称最小二乘平滑模型和一个改进的对称最小二乘平滑模型的加权算法来确定插值信号的基线。该算法通过六位专家产科医生注释确定的真实基准进行了验证。ARDASIAsLS 方法的 FHR 基线计算性能产生了 2.54 bpm 的平均绝对误差、5.22 bpm 的最大绝对误差和 2.89 bpm 的均方根误差。在算法与 11 种先前发表的方法进行的比较中，该算法优于所有其他方法。值得注意的是，该算法与专家注释不相上下。自动化 FHR 基线确定过程可以帮助减少从业者之间的分歧，并在分娩室辅助决策。
