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Feng G, Heiselman C, Quirk JG, Djurić PM. Cardiotocography analysis by empirical dynamic modeling and Gaussian processes. Front Bioeng Biotechnol 2023; 10:1057807. [PMID: 36714626 PMCID: PMC9877465 DOI: 10.3389/fbioe.2022.1057807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature of these recordings, the evaluation by obstetricians suffers from high interobserver and intraobserver variability. Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. Methods: Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. In this paper, we propose to model intrapartum CTG recordings from a dynamical system perspective using empirical dynamic modeling with Gaussian processes, which is a Bayesian nonparametric approach for estimation of functions. Results and Discussion: In the context of our paper, Gaussian processes are capable for simultaneous estimation of the dimensionality of attractor manifolds and reconstructing of attractor manifolds from time series data. This capacity of Gaussian processes allows for revealing causal relationships between the studied time series. Experimental results on real CTG recordings show that FHR and UA signals are causally related. More importantly, this causal relationship and estimated attractor manifolds can be exploited for several important applications in computerized analysis of CTG recordings including estimating missing FHR samples, recovering burst errors in FHR tracings and characterizing the interactions between FHR and UA signals.
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Affiliation(s)
- Guanchao Feng
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States,*Correspondence: Guanchao Feng, ; Petar M. Djurić,
| | - Cassandra Heiselman
- Department of Obstetrics and Gynecology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - J. Gerald Quirk
- Department of Obstetrics and Gynecology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Petar M. Djurić
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States,*Correspondence: Guanchao Feng, ; Petar M. Djurić,
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Olmos-Ramírez RL, Peña-Castillo MÁ, Mendieta-Zerón H, Reyes-Lagos JJ. Uterine activity modifies the response of the fetal autonomic nervous system at preterm active labor. Front Endocrinol (Lausanne) 2022; 13:1056679. [PMID: 36714609 PMCID: PMC9882419 DOI: 10.3389/fendo.2022.1056679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/20/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND The autonomic nervous system of preterm fetuses has a different level of maturity than term fetuses. Thus, their autonomic response to transient hypoxemia caused by uterine contractions in labor may differ. This study aims to compare the behavior of the fetal autonomic response to uterine contractions between preterm and term active labor using a novel time-frequency analysis of fetal heart rate variability (FHRV). METHODS We performed a case-control study using fetal R-R and uterine activity time series obtained by abdominal electrical recordings from 18 women in active preterm labor (32-36 weeks of gestation) and 19 in active term labor (39-40 weeks of gestation). We analyzed 20 minutes of the fetal R-R time series by applying a Continuous Wavelet Transform (CWT) to obtain frequency (HF, 0.2-1 Hz; LF, 0.05-0.2 Hz) and time-frequency (Flux0, Flux90, and Flux45) domain features. Time domain FHRV features (SDNN, RMSSD, meanNN) were also calculated. In addition, ultra-short FHRV analysis was performed by segmenting the fetal R-R time series according to episodes of the uterine contraction and quiescent periods. RESULTS No significant differences between preterm and term labor were found for FHRV features when calculated over 20 minutes. However, we found significant differences when segmenting between uterine contraction and quiescent periods. In the preterm group, the LF, Flux0, and Flux45 were higher during the average contraction episode compared with the average quiescent period (p<0.01), while in term fetuses, vagally mediated FHRV features (HF and RMSSD) were higher during the average contraction episode (p<0.05). The meanNN was lower during the strongest contraction in preterm fetuses compared to their consecutive quiescent period (p=0.008). CONCLUSION The average autonomic response to contractions in preterm fetuses shows sympathetic predominance, while term fetuses respond through parasympathetic activity. Comparison between groups during the strongest contraction showed a diminished fetal autonomic response in the preterm group. Thus, separating contraction and quiescent periods during labor allows for identifying differences in the autonomic nervous system cardiac regulation between preterm and term fetuses.
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Affiliation(s)
- Rocio Lizbeth Olmos-Ramírez
- Basic Sciences and Engineering Division, Metropolitan Autonomous University (UAM) Campus Iztapalapa, Mexico City, Mexico
| | - Miguel Ángel Peña-Castillo
- Basic Sciences and Engineering Division, Metropolitan Autonomous University (UAM) Campus Iztapalapa, Mexico City, Mexico
| | - Hugo Mendieta-Zerón
- Health Institute of the State of Mexico (ISEM), "Mónica Pretelini Sáenz" Maternal-Perinatal Hospital, Toluca, Mexico
- School of Medicine, Autonomous University of the State of Mexico (UAEMéx), Toluca, Mexico
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Chiera M, Cerritelli F, Casini A, Barsotti N, Boschiero D, Cavigioli F, Corti CG, Manzotti A. Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review. Front Neurosci 2020; 14:561186. [PMID: 33071738 PMCID: PMC7544983 DOI: 10.3389/fnins.2020.561186] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/28/2020] [Indexed: 12/18/2022] Open
Abstract
Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.
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Affiliation(s)
- Marco Chiera
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Research Commission on Manual Therapies and Mind-Body Disciplines, Societ Italiana di Psico Neuro Endocrino Immunologia, Rome, Italy
| | - Francesco Cerritelli
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy
| | - Alessandro Casini
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy
| | - Nicola Barsotti
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Research Commission on Manual Therapies and Mind-Body Disciplines, Societ Italiana di Psico Neuro Endocrino Immunologia, Rome, Italy
| | | | - Francesco Cavigioli
- Neonatal Intensive Care Unit, "V. Buzzi" Children's Hospital, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy
| | - Carla G Corti
- Pediatric Cardiology Unit-Pediatric Department, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy
| | - Andrea Manzotti
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Neonatal Intensive Care Unit, "V. Buzzi" Children's Hospital, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy.,Research Department, SOMA, Istituto Osteopatia Milano, Milan, Italy
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Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9245421] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.
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Saleem S, Naqvi SS, Manzoor T, Saeed A, ur Rehman N, Mirza J. A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings. Front Physiol 2019; 10:246. [PMID: 30941054 PMCID: PMC6433745 DOI: 10.3389/fphys.2019.00246] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 02/25/2019] [Indexed: 11/13/2022] Open
Abstract
We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.
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Affiliation(s)
- Saqib Saleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal, Pakistan
| | - Syed Saud Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Tareq Manzoor
- Energy Research Center, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ahmed Saeed
- School of Computing, Ulster University, Newtownabbey, United Kingdom
| | - Naveed ur Rehman
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Jawad Mirza
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
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Cömert Z, Kocamaz AF, Subha V. Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment. Comput Biol Med 2018; 99:85-97. [PMID: 29894897 DOI: 10.1016/j.compbiomed.2018.06.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 05/20/2018] [Accepted: 06/03/2018] [Indexed: 11/25/2022]
Abstract
Cardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. In this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.
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Affiliation(s)
- Zafer Cömert
- Bitlis Eren University, Department of Computer Engineering, Bitlis, Turkey.
| | | | - Velappan Subha
- Manonmaniam Sundaranar University, Department of Computer Science and Engineering, India.
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Warmerdam GJJ, Vullings R, Van Laar JOEH, Van der Hout-Van der Jagt MB, Bergmans JWM, Schmitt L, Oei SG. Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis. Physiol Meas 2018; 39:025008. [PMID: 29350194 DOI: 10.1088/1361-6579/aaa925] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Monitoring of the fetal condition during labor is currently performed by cardiotocograpy (CTG). Despite the use of CTG in clinical practice, CTG interpretation suffers from a high inter- and intra-observer variability and a low specificity. In addition to CTG, analysis of fetal heart rate variability (HRV) has been shown to provide information on fetal distress. However, fetal HRV can be strongly influenced by uterine contractions, particularly during the second stage of labor. Therefore, the aim of this study is to examine if distinguishing contractions from rest periods can improve the detection rate of HRV features for fetal distress during the second stage of labor. APPROACH We used a dataset of 100 recordings, containing 20 cases of fetuses with adverse outcome. The most informative HRV features were selected by a genetic algorithm and classification performance was evaluated using support vector machines. MAIN RESULTS Classification performance of fetal heart rate segments closest to birth improved from a geometric mean of 70% to 79%. If the classifier was used to indicate fetal distress over time, the geometric mean at 15 minutes before birth improved from 60% to 72%. SIGNIFICANCE Our results show that combining contraction-dependent HRV features with HRV features calculated over the entire fetal heart rate signal improves the detection rate of fetal distress.
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Affiliation(s)
- G J J Warmerdam
- Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Jongen GJLM, van der Hout-van der Jagt MB, Oei SG, van de Vosse FN, Bovendeerd PHM. Simulation of fetal heart rate variability with a mathematical model. Med Eng Phys 2017; 42:55-64. [PMID: 28196652 DOI: 10.1016/j.medengphy.2017.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 12/23/2016] [Accepted: 01/17/2017] [Indexed: 11/28/2022]
Abstract
In the clinic, the cardiotocogram (CTG), the combined registration of fetal heart rate (FHR) and uterine contractions, is used to predict fetal well-being. Amongst others, fetal heart rate variability (FHRV) is an important indicator of fetal distress. In this study we add FHRV to our previously developed CTG simulation model, in order to improve its use as a research and educational tool. We implemented three sources of variability by applying either 1/f or white noise to the peripheral vascular resistance, baroreceptor output, or efferent vagal signal. Simulated FHR tracings were evaluated by visual inspection and spectral analysis. All power spectra showed a 1/f character, irrespective of noise type and source. The clinically observed peak near 0.1 Hz was only obtained by applying white noise to the different sources of variability. Similar power spectra were found when peripheral vascular resistance or baroreceptor output was used as source of variability. Sympathetic control predominantly influenced the low frequency power, while vagal control influenced both low and high frequency power. In contrast to clinical data, model results did not show an increase of FHRV during FHR decelerations. Still, addition of FHRV improves the applicability of the model as an educational and research tool.
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Affiliation(s)
- Germaine J L M Jongen
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands; Department of Gynecology and Obstetrics, Máxima Medical Center, PO Box 7777, 5500 MB Veldhoven, The Netherlands.
| | - M Beatrijs van der Hout-van der Jagt
- Department of Gynecology and Obstetrics, Máxima Medical Center, PO Box 7777, 5500 MB Veldhoven, The Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - S Guid Oei
- Department of Gynecology and Obstetrics, Máxima Medical Center, PO Box 7777, 5500 MB Veldhoven, The Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - Frans N van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - Peter H M Bovendeerd
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands.
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Romano M, Iuppariello L, Ponsiglione AM, Improta G, Bifulco P, Cesarelli M. Frequency and Time Domain Analysis of Foetal Heart Rate Variability with Traditional Indexes: A Critical Survey. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:9585431. [PMID: 27195018 PMCID: PMC4852340 DOI: 10.1155/2016/9585431] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 03/06/2016] [Accepted: 03/07/2016] [Indexed: 11/17/2022]
Abstract
Monitoring of foetal heart rate and its variability (FHRV) covers an important role in assessing health of foetus. Many analysis methods have been used to get quantitative measures of FHRV. FHRV has been studied in time and in frequency domain and interesting clinical results have been obtained. Nevertheless, a standardized definition of FHRV and a precise methodology to be used for its evaluation are lacking. We carried out a literature overview about both frequency domain analysis (FDA) and time domain analysis (TDA). Then, by using simulated FHR signals, we defined the methodology for FDA. Further, employing more than 400 real FHR signals, we analysed some of the most common indexes, Short Term Variability for TDA and power content of the spectrum bands and sympathovagal balance for FDA, and evaluated their ranges of values, which in many cases are a novelty. Finally, we verified the relationship between these indexes and two important parameters: week of gestation, indicator of foetal growth, and foetal state, classified as active or at rest. Our results indicate that, according to literature, it is necessary to standardize the procedure for FHRV evaluation and to consider week of gestation and foetal state before FHR analysis.
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Affiliation(s)
- Maria Romano
- DMSC, University “Magna Graecia”, Catanzaro, Italy
| | | | | | - Giovanni Improta
- Department of Public Health, University of Naples “Federico II” Hospital, Naples, Italy
| | - Paolo Bifulco
- DIETI, University of Naples “Federico II”, Naples, Italy
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Warmerdam GJJ, Vullings R, Van Laar JOEH, Van der Hout-Van der Jagt MB, Bergmans JWM, Schmitt L, Oei SG. Using uterine activity to improve fetal heart rate variability analysis for detection of asphyxia during labor. Physiol Meas 2016; 37:387-400. [PMID: 26862891 DOI: 10.1088/0967-3334/37/3/387] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
During labor, uterine contractions can cause temporary oxygen deficiency for the fetus. In case of severe and prolonged oxygen deficiency this can lead to asphyxia. The currently used technique for detection of asphyxia, cardiotocography (CTG), suffers from a low specificity. Recent studies suggest that analysis of fetal heart rate variability (HRV) in addition to CTG can provide information on fetal distress. However, interpretation of fetal HRV during labor is difficult due to the influence of uterine contractions on fetal HRV. The aim of this study is therefore to investigate whether HRV features differ during contraction and rest periods, and whether these differences can improve the detection of asphyxia. To this end, a case-control study was performed, using 14 cases with asphyxia that were matched with 14 healthy fetuses. We did not find significant differences for individual HRV features when calculated over the fetal heart rate without separating contractions and rest periods (p > 0.30 for all HRV features). Separating contractions from rest periods did result in a significant difference. In particular the ratio between HRV features calculated during and outside contractions can improve discrimination between fetuses with and without asphyxia (p < 0.04 for three out of four ratio HRV features that were studied in this paper).
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Affiliation(s)
- G J J Warmerdam
- Faculty of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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