1,720,979 research outputs found
Multi-parametric cardiorespiratory analysis in late-preterm, early-term, and full-term infants at birth
No full textInfants born at 35–37 weeks’ gestational age (GA) are at higher risk for a range of pathological conditions and poorer neurodevelopmental outcomes. However, mechanisms responsible are not fully understood. The purpose of this paper is to use traditional and novel techniques to assess newborn autonomic development as a function of GA at birth, focusing on cardiorespiratory regulation. ECG and respiration were acquired during sleep on 329 healthy newborns. Infants were divided into GA groups: 35–36 weeks (late preterm (LPT)), 37–38 weeks (early term (ET)), and 39–40 weeks (full term (FT)). Time domain, frequency domain, and non-linear measures were calculated. Increased heart rate short-term variability and complexity as a function of GA were observed in time domain and non-linear measures. Decreasing inter-breath interval variability was found as a function of GA, with increasing linear cardiorespiratory coupling. A complexity parameter (quadratic sample entropy) was less affected by arrhythmias and artifacts when compared to traditional measures. Results suggest lower maturation in LPT, with less developed cardiorespiratory regulation. This may confer risk for altered outcome, convergent with epidemiological findings. Reported examples show that a combination of methodological approaches can be beneficial to characterize autonomic maturation. [Figure not available: see fulltext.
Complex and nonlinear analysis of heart rate variability in the assessment of fetal and neonatal wellbeing
Get PDFStudy of fetal and neonatal heart rate variability offers a powerful tool to monitor, even indirectly, the physiological control mechanisms playing a crucial role in the early development of human life. Moreover, sleep state analysis is a challenging topic as both quality of the sleep and its relationship with neural development and control can provide information potentially useful to the clinical environment. This contribution aims at presenting major advances in heart rate variability analysis in both fetuses and newborn population during sleep. The important role of quantitative multi-parameter analysis methods will be discussed with reference to new monitoring and diagnostic solutions. Results will be presented and discussed showing how sleep dynamics are related to cardiovascular control systems with the major focus of deeper investigating the development of human life in its early stages
An Efficient Algorithm for the Extraction of Fetal ECG from Standard and Non-Standard Multi Abdominal Maternal Leads
No full textThe importance of fetal surveillance during pregnancy is worldwide accepted since its peculiar ability to anticipate fetal distress under a variety of conditions. The novel frontier in the field of remote fetal monitoring relies on a continuous and everyday-monitoring of fetal wellbeing. As a consequence, fECG monitoring systems have seen a net increase in popularity in the recent years. In this paper, we propose a novel algorithm for the detection of fECG and we validated its performances by testing it on an open source collection of 75 annotated fECG traces. Our results show the reliability of the proposed methodology in extracting fECG and deriving an estimate of fHR
Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model
Get PDFObjective: 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. Materials and methods: 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. Results: 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. Discussion: 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. Conclusion: The inclusion of DL methods on CTG signals may complement imaging technologies and improve the early detection of IUGR
Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses
Get PDFThe presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR signal is equal 2 Hz. The recorded populations consist of two groups of fetuses: 60 healthy and 60 Intra Uterine Growth Restricted (IUGR) fetuses. IUGR condition is a fetal condition defined as the abnormal rate of fetal growth. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. The pathology is a documented cause of fetal and neonatal morbidity and mortality. The described database was employed in a set of machine learning approaches for the early detection of the IUGR condition: “Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring” [1]. The added value of the proposed indices is their interpretability and close connection to physiological and pathological aspect of FHR regulation. Additional information on data acquisition, feature extraction and potential relevance in clinical practice are discussed in [1]
Transfer Entropy Modeling of Newborn Cardiorespiratory Regulation
Get PDF3Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
This study investigates the complex interplay between the cardiac and respiratory systems in 268 healthy neonates born between 35 and 40 weeks of gestation. The aim is to provide a comprehensive description of the developing cardiorespiratory information transfer mechanisms as a function of gestational age (GA). This report proposes an extension of the traditional Transfer Entropy measure (TE), which employs multiple lagged versions of the time series of the intervals between two successive R waves of the QRS signal on the electrocardiogram (RR series) and respiration time series (RESP). The method aims to quantify the instantaneous and delayed effects between the two processes within a fine-grained time scale. Firstly, lagged TE was validated on a simulated dataset. Subsequently, lagged TE was employed on newborn cardiorespiratory data. Results indicate a progressive increase in information transfer as a function of gestational age, as well as significant differences in terms of instantaneous and delayed interactions between the cardiac and the respiratory system when comparing the two TE directionalities (RR→RESP vs. RESP→RR). The proposed investigation addresses the role of the different autonomic nervous system (ANS) branches involved in the cardiorespiratory system, since the sympathetic and parasympathetic branches operate at different time scales. Our results allow to infer that the two TE directionalities are uniquely and differently modulated by both branches of the ANS. TE adds an original quantitative tool to understanding cardiorespiratory imbalance in early infanc
A Machine Learning Approach to Monitor the Emergence of Late Intrauterine Growth Restriction
Get PDFLate intrauterine growth restriction (IUGR) is a fetal pathological condition characterized by
chronic hypoxia secondary to placental insufficiency, resulting in an abnormal rate of fetal
growth. This pathology has been associated with increased fetal and neonatal morbidity
and mortality. In standard clinical practice, late IUGR diagnosis can only be suspected in
the third trimester and ultimately confirmed at birth. This study presents a radial basis
function support vector machine (RBF-SVM) classification based on quantitative features
extracted from fetal heart rate (FHR) signals acquired using routine cardiotocography
(CTG) in a population of 160 healthy and 102 late IUGR fetuses. First, the individual
performance of each time, frequency, and nonlinear feature was tested. To improve the
unsatisfactory results of univariate analysis we firstly adopted a Recursive Feature
Elimination approach to select the best subset of FHR-based parameters contributing
to the discrimination of healthy vs. late IUGR fetuses. A fine tuning of the RBF-SVM model
parameters resulted in a satisfactory classification performance in the training set
(accuracy 0.93, sensitivity 0.93, specificity 0.84). Comparable results were obtained
when applying the model on a totally independent testing set. This investigation
supports the use of a multivariate approach for the in utero identification of late IUGR
condition based on quantitative FHR features encompassing different domains. The
proposed model allows describing the relationships among features beyond the
traditional linear approaches, thus improving the classification performance. This
framework has the potential to be proposed as a screening tool for the identification of
late IUGR fetuses
A semi-Supervised Deep Learning Approach to Automate the Identification of Fetal Behavioral States in Fetal Heart Rate Tracings
No full textComputerized Cardiotocography (cCTG) facilitates a thorough and objective examination of the Fetal Heart Rate (FHR), providing valuable insights into the fetal condition and its well-being. A crucial aspect within this context pertains to the automatic identification of periods of fetal activity and quiescence, which are associated with different FHR patterns. The accurate discrimination of these patterns holds the potential to improve the interpretability and diagnostic capabilities of FHR quantitative analysis. Indeed, disruptions in the cycling between active and quiet periods are associated with the development of pathological conditions. This study introduces a deep learning based methodology for the identification of fetal behavioral heart rate patterns. Specifically, the implemented deep neural network (DNN) adopts a 1D encoder-decoder architecture, which is trained to recognize and automatically segment the FHR recordings into active and quiet periods. The proposed framework includes a semi-supervised training process, based on two steps: a) DNN pre-training based on pseudo-labels generated by a Hidden Markov Model (HMM), b) DNN fine-tuning integrating the annotations of an expert Ob-Gyn clinician. The trained DNN exhibits promising results: Balanced Accuracy of 88.37%, Macro F1-Score of 87.87% and Matthews Correlation Coefficient (MCC) of 75.80% on a distinct hold-out test set, encompassing 45 FHR traces annotated by an expert Ob-Gyn clinician
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