## 1 Introduction  
Electronic fetal monitoring (EFM) is currently the primary method used to monitor the well-being of a fetus during labor. This method involves the continuous monitoring of two vital signals: the fetal heart rate (FHR) and maternal uterine contractions (UC) [1]. However, traditional methods of predicting fetal asphyxia based on the visual interpretation of FHR recordings or the categorization of FHR parameters have limited accuracy [2]. The objective of studying FHR signals is to quantify the amount of information they contain. The accuracy of traditional categorization systems for predicting acidemia based on FHR parameters, such as guidelines like those of the American College of Obstetricians and Gynecologists (ACOG) [3,4], is limited [5]. Additionally, there is low interobserver agreement among experts [6]. Therefore, modeling EFM characteristics using machine learning algorithms has become more important than visual interpretation of the signal. Recent efforts to improve the diagnosis of acidemia have focused on proposing novel predictors derived from fetal cardiotocography (CTG) and integrating them with existing features. Previous studies have shown that factors such as deceleration physiology and specific parameters like deceleration area or fetal resilience are important in predicting fetal asphyxia [7, 8, 9]. The total area of deceleration [7,8] holds special relevance as it reflects the cumulative duration and severity of decelerations observed during fetal monitoring. Decelerations in the fetal heart rate serve as indicators of potential distress or compromised oxygen supply to the fetus. The duration of reperfusion [9], which refers to the restoration of blood flow, can impact cerebral oxygenation. Short intervals between decelerations (less than 2-3 min) may lead to fetal adaptation towards acidosis (an increase in blood acid levels) and progressive hypotension (low blood pressure). This implies that inadequate time for reperfusion between decelerations can have negative effects on fetal well-being. Animal models, such as sheep fetuses, have provided insights into the fetal heart rate response to hypoxia [10]. Characteristics of decelerations, including their duration, severity, and shape, offer valuable information about the fetal adaptive response and the extent of oxygen deprivation. The deceleration pattern in the fetal heart rate can vary based on the duration and severity of hypoxia. Furthermore, the shape and slope of the deceleration pattern, particularly the descending limb, exhibit changes in response to hypoxia. These animal models have also shed light on the underlying physiological mechanisms associated with the fetal heart rate response to hypoxia. Sensitization to the vagal response of the chemoreflex has been implicated in the increased slope of decelerations during prolonged hypoxia. This suggests that the activation of specific neural pathways and chemoreceptors plays a role in modulating the fetal heart rate during oxygen deprivation. Moreover, the final window slope is considered a valuable tool for grading the fetal adaptive response during childbirth [11]. This indicates that the slope of the deceleration pattern can provide crucial information about the fetal response to the stressors encountered during labor and delivery. Additionally, automated systems have the capability to extract data on the fetal heart rate (FHR) [12], and signal processing techniques like fractal analysis can be utilized to identify patterns [13,14]. Machine learning algorithms possess the ability to handle vast amounts of data derived from the FHR signal and identify patterns that may not be easily discernible through traditional methods, enabling the analysis of complex relationships [15]. This is particularly crucial in predicting acidemia, as subtle changes in the FHR signal can indicate the onset of fetal distress [16,17].