Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states. We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals. The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%. The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis.

Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk. Graphical

Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth restriction (FGR). Antenatal cardiotocography (CTG) can aid in the early detection of IUGR. Reliably diagnosing IUGR before delivery remains challenging, and deep learning (DL) techniques offer potential solutions. This paper describes the development of a DL approach to predict an IUGR condition at birth by using CTG signals collected during antenatal monitoring. Our method is encapsulated in the concept of a two-step training process of a ResNet architecture. The primary focus is on the minimization of data loss, which motivates the division into "presumed" and "confirmed" datasets, which is employed to distinguish based on the presence of information at birth. The method involves fine-tuning: the initial training utilizes "presumed" data to train the network, and the subsequent training employs data representing certain knowledge to refine its performance. The DL model reaches a balanced accuracy of 80% on a hold-out test set of confirmed cases, which is better than what obtained by using standard clinical guidelines. The results of our work are compared to the results of similar papers dealing with the prediction of IUGR condition at birth and in general with the prediction of fetal pathological conditions. Our final results are obtained using a very large dataset compared to other papers reported in the literature. The inclusion of DL methods on CTG signals may complement imaging technologies and improve the early detection of IUGR.

Fetal acidemia, caused by impaired gas exchange between the fetus and the mother, is a leading cause of stillbirth and neurologic complications. Early prediction is therefore essential to guide timely clinical intervention. Several strategies rely on cardiotocography (CTG), which combines fetal heart rate (fHR) with uterine contractions and has led to development of clinical guidelines for CTG interpretation and the introduction of different fHR features. Additionally, ST event analysis, investigating changes in the ST segments of the fetal electrocardiogram (fECG), has been proposed as a complementary tool. This narrative review adopts a systematic approach, with comprehensive searches in Embase and PubMed to ensure full coverage of the available literature, and summarizes findings from 30 studies. Clinical guidelines for CTG interpretation frequently lead to intermediate risk level annotations, leaving the final decision regarding fetal management to clinical experience. In contrast, various fHR features can successfully discriminate between fetuses developing acidemia and healthy controls. Evidence regarding the added value of ST events derived from the scalp electrode remains conflicting, due to concerns about invasiveness. Recent studies on machine learning models highlight their ability to integrate multiple fHR features and improve predictive performance, suggesting a promising direction for enhancing acidemia prediction during labor.

Maternal Fetal Cardiac Coupling (MFCC) is an understudied physiological phenomenon that could play a key role in the understanding and early detection of pregnancy-related pathologies. In this study, we aimed to investigate the role of maternal respiration in the short-term directed interactions between the maternal heart rate (MHR) and fetal heart rate (FHR) in healthy fetuses. Additionally, we investigated how such coupling patterns might be impaired in fetuses affected by conotruncal anomalies (CTA). FHR, MHR and maternal respiration were extracted from non-invasive abdominal electrophysiological recordings from over 300 pregnant women in the second trimester of gestation. Linear coupling metrics-Directed Coherence (DC), Partial Directed Coherence (PDC), and generalized PDC (gPDC)-were computed with and without including the respiratory signal in the Multivariate Autoregressive model. Results between healthy and CTA fetuses were compared. Maternal respiration Granger-causes both the FHR and the MHR, suggesting respiration is a dominant source of directional influence in MFCC during the second trimester. Preliminary results suggest that the directed interaction from maternal respiration to the FHR is reduced in fetuses with CTA. Maternal respiration plays a main role in the physiological phenomenon of MFCC. This work sheds light on a fundamental dynamic regulating MFCC, which might improve the monitoring of pregnancy-related cardiovascular pathologies and pave the way for their early diagnosis.

Fetal behavioral states reflect the developing brain’s capacity to organize behavior into distinct sleep state patterns and are a window to evaluate the maturation of the nervous system throughout gestation. This study presents a deep learning-based methodology for the identification of behavioral states from fetal heart rate traces (FHR) recorded via cardiotocography (CTG). A Residual Attention U-Net was pre-trained on a large unlabeled dataset of over 7000 FHR recordings and fine-tuned on a multi-center dataset comprising 236 FHR signals with labeled states. These signals were collected across multiple countries using different cardiotocographs and annotated by clinicians with diverse backgrounds. The model showed high agreement with expert clinicians on a stratified hold-out test set, averaging a Macro F1-Score of 91% and Balanced Accuracy of 93% in distinguishing between Active and Quiet states. The model has the potential to enhance the evaluation of fetal behavioral states in clinical practice by assisting clinicians and ensuring objective, rapid, and reproducible results, as well as supporting future enhancements in electronic fetal monitoring.