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Nieto-Del-Amor F, Ye-Lin Y, Monfort-Ortiz R, Diago-Almela VJ, Modrego-Pardo F, Martinez-de-Juan JL, Hao D, Prats-Boluda G. Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108317. [PMID: 38996804 DOI: 10.1016/j.cmpb.2024.108317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice. METHODS For this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model's predictions were then fine-tuned by post-processing. RESULTS U-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model's adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals. CONCLUSIONS As automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes.
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Affiliation(s)
- Félix Nieto-Del-Amor
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China
| | | | | | | | - Jose L Martinez-de-Juan
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China
| | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China.
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Vinothini S, Punitha N, Karthick PA, Ramakrishnan S. Cyclostationary analysis of uterine EMG measurements for the prediction of preterm birth. Biomed Eng Lett 2024; 14:727-736. [PMID: 38946820 PMCID: PMC11208349 DOI: 10.1007/s13534-024-00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 07/02/2024] Open
Abstract
Preterm birth (gestational age < 37 weeks) is a public health concern that causes fetal and maternal mortality and morbidity. When this condition is detected early, suitable treatment can be prescribed to delay labour. Uterine electromyography (uEMG) has gained a lot of attention for detecting preterm births in advance. However, analyzing uEMG is challenging due to the complexities associated with inter and intra-subject variations. This work aims to investigate the applicability of cyclostationary characteristics in uEMG signals for predicting premature delivery. The signals under term and preterm situations are considered from two online datasets. Preprocessing is carried out using a Butterworth bandpass filter, and spectral correlation density function is adapted using fast Fourier transform-based accumulation method (FAM) to compute the cyclostationary variations. The cyclic frequency spectral density (CFSD) and degree of cyclostationarity (DCS) are quantified to assess the existence of cyclostationarity. Features namely, maximum cyclic frequency, bandwidth, mean cyclic frequency (MNCF), and median cyclic frequency (MDCF) are extracted from the cyclostationary spectrum and analyzed statistically. uEMG signals exhibit cyclostationarity property, and these variations are found to distinguish preterm from term conditions. All the four extracted features are noted to decrease from term to preterm conditions. The results indicate that the cyclostationary nature of the signals can provide better characterization of uterine muscle contractions and could be helpful in detecting preterm birth. The proposed method appears to aid in detecting preterm birth, as analysis of uterine contractions under preterm conditions is imperative for timely medical intervention.
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Affiliation(s)
- S Vinothini
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - N Punitha
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India
| | - P A Karthick
- Department of Instrumentation and Control, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India
| | - S Ramakrishnan
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India
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Jager F. An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery. Sci Data 2023; 10:669. [PMID: 37783671 PMCID: PMC10545725 DOI: 10.1038/s41597-023-02581-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
The existing non-invasive automated preterm birth prediction methods rely on the use of uterine electrohysterogram (EHG) records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean section deliveries. In order to enhance current publicly available pool of term EHG records, we developed a new EHG dataset, Induced Cesarean EHG DataSet (ICEHG DS), containing 126 30-minute EHG records, recorded early (23rd week), and/or later (31st week) during pregnancy, of those pregnancies that were expected to end in spontaneous term delivery, but ended in induced or cesarean section delivery. The records were collected at the University Medical Center Ljubljana, Ljubljana, Slovenia. The dataset includes 38 and 43, early and later, induced; 11 and 8, early and later, cesarean; and 13 and 13, early and later, induced and cesarean EHG records. This dataset enables better understanding of the underlying physiological mechanisms involved during pregnancies ending in induced and cesarean deliveries, and provides a robust and more realistic assessment of the performance of automated preterm birth prediction methods.
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Affiliation(s)
- Franc Jager
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia.
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4
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Karthick PA, Selvaraju V, Swaminathan R. Empirical Mode Decomposition Based Measures for Investigating the Progression of Pregnancy from Uterine EMG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083708 DOI: 10.1109/embc40787.2023.10340089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The objective of this study is to analyze the uterine electromyography (uEMG) signals to study the progression of pregnancy under term condition (gestational age > 36 weeks) using EMD-based time-frequency features. uEMG signals are obtained from the multiple public datasets during two conditions, namely T1 (acquired < 26 gestational weeks) and T2 (acquired ≥ 26 gestational weeks). The considered signals are preprocessed. Empirical mode decomposition is applied to decompose the signals and time-frequency features, such as median frequency (MDF), mean frequency (MNF), peak frequency and peak magnitude, are extracted from each intrinsic mode functions and statistically analyzed. The results depict that the obtained time-frequency features are able to distinguish between T1 and T2 conditions. The extracted features, namely MNF and MDF, are observed to decrease from T1 to T2 conditions. These features are found to have higher effect size, confirming the better differentiation between T1 and T2 conditions. It appears that EMD-based time-frequency features can aid in studying the evolving changes in uterine contractions towards labor.
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5
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Nsugbe E, Reyes‐Lagos JJ, Adams D, Samuel OW. On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines. Healthc Technol Lett 2023; 10:11-22. [PMID: 37077881 PMCID: PMC10107387 DOI: 10.1049/htl2.12044] [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: 12/19/2022] [Revised: 03/03/2023] [Accepted: 03/23/2023] [Indexed: 04/21/2023] Open
Abstract
Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health-based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre-processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.
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Affiliation(s)
| | | | - Dawn Adams
- School of ComputingUlster UniversityNewtownabbeyUK
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6
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Goldsztejn U, Nehorai A. Predicting preterm births from electrohysterogram recordings via deep learning. PLoS One 2023; 18:e0285219. [PMID: 37167222 PMCID: PMC10174487 DOI: 10.1371/journal.pone.0285219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/18/2023] [Indexed: 05/13/2023] Open
Abstract
About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm births more than one week in advance remains elusive. Here, we develop a deep learning method to predict preterm births directly from electrohysterogram (EHG) measurements of pregnant mothers recorded at around 31 weeks of gestation. We developed a prediction model, which includes a recurrent neural network, to predict preterm births using short-time Fourier transforms of EHG recordings and clinical information from two public datasets. We predicted preterm births with an area under the receiver-operating characteristic curve (AUC) of 0.78 (95% confidence interval: 0.76-0.80). Moreover, we found that the spectral patterns of the measurements were more predictive than the temporal patterns, suggesting that preterm births can be predicted from short EHG recordings in an automated process. We show that preterm births can be predicted for pregnant mothers around their 31st week of gestation, prompting beneficial treatments to reduce the incidence of preterm births and improve their outcomes.
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Affiliation(s)
- Uri Goldsztejn
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Arye Nehorai
- Preston M. Green Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, United States of America
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Diaz-Martinez A, Monfort-Ortiz R, Ye-Lin Y, Garcia-Casado J, Nieto-Tous M, Nieto-Del-Amor F, Diago-Almela V, Prats-Boluda G. Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Pirnar Ž, Jager F, Geršak K. Characterization and separation of preterm and term spontaneous, induced, and cesarean EHG records. Comput Biol Med 2022; 151:106238. [PMID: 36343404 DOI: 10.1016/j.compbiomed.2022.106238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
To improve the understanding of the underlying physiological processes that lead to preterm birth, and different term delivery modes, we quantitatively characterized and assessed the separability of the sets of early (23rd week) and later (31st week) recorded, preterm and term spontaneous, induced, cesarean, and induced-cesarean electrohysterogram (EHG) records using several of the most widely used non-linear features extracted from the EHG signals. Linearly modeled temporal trends of the means of the median frequencies (MFs), and of the means of the peak amplitudes (PAs) of the normalized power spectra of the EHG signals, along pregnancy (from early to later recorded records), derived from a variety of frequency bands, revealed that for the preterm group of records, in comparison to all other term delivery groups, the frequency spectrum of the frequency band B0L (0.08-0.3 Hz) shifts toward higher frequencies, and that the spectrum of the newly identified frequency band B0L' (0.125-0.575 Hz), which approximately matches the Fast Wave Low band, becomes stronger. The most promising features to separate between the later preterm group and all other later term delivery groups appear to be MF (p=1.1⋅10-5) in the band B0L of the horizontal signal S3, and PA (p=2.4⋅10-8) in the band B0L' (S3). Moreover, the PA in the band B0L' (S3) showed the highest power to individually separate between the later preterm group and any other later term delivery group. Furthermore, the results suggest that in preterm pregnancies the resting maternal heart rate decreases between the 23rd and 31st week of gestation.
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Affiliation(s)
- Žiga Pirnar
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
| | - Franc Jager
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia.
| | - Ksenija Geršak
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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9
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Paljk Likar I, Becic E, Pezdirc N, Gersak K, Lucovnik M, Trojner Bregar A. Comparison of Oxytocin vs. Carbetocin Uterotonic Activity after Caesarean Delivery Assessed by Electrohysterography: A Randomised Trial. SENSORS (BASEL, SWITZERLAND) 2022; 22:8994. [PMID: 36433591 PMCID: PMC9698977 DOI: 10.3390/s22228994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/11/2022] [Accepted: 11/19/2022] [Indexed: 06/16/2023]
Abstract
Electrohysterography has been used for monitoring uterine contractility in pregnancy and labour. Effective uterine contractility is crucial for preventing postpartum haemorrhage. The objective of our study was to compare postpartum electrohysterograms in women receiving oxytocin vs. carbetocin for postpartum haemorrhage prevention after caesarean delivery. The trial is registered at ClinicalTrials.gov with the identifier NCT04201665. We included 64 healthy women with uncomplicated singleton pregnancies at term scheduled for caesarean section after one previous caesarean section. After surgery, a 15 min electrohysterogram was obtained after which women were randomised to receive either five IU of oxytocin intravenously or 100 μg of carbetocin intramuscularly. A 30 min electrohysterogram was performed two hours after drug application. Changes in power density spectrum peak frequency of electrohysterogram pseudo-bursts were analysed. A significant reduction in power density spectrum peak frequency in the first two hours was observed after carbetocin but not after oxytocin (median = 0.07 (interquartile range (IQR): 0.87 Hz) compared to median = -0.63 (IQR: 0.20) Hz; p = 0.004). Electrohysterography can be used for objective comparison of uterotonic effects. We found significantly higher power density spectrum peak frequency two hours after oxytocin compared to carbetocin.
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Affiliation(s)
- Ivana Paljk Likar
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
- Division of Obstetrics and Gynecology, Department of Perinatology, University Medical Centre Ljubljana, 1000 Ljubljana, Slovenia
| | - Emra Becic
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Neza Pezdirc
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Ksenija Gersak
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
- Division of Obstetrics and Gynecology, Department of Perinatology, University Medical Centre Ljubljana, 1000 Ljubljana, Slovenia
| | - Miha Lucovnik
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
- Division of Obstetrics and Gynecology, Department of Perinatology, University Medical Centre Ljubljana, 1000 Ljubljana, Slovenia
| | - Andreja Trojner Bregar
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
- Division of Obstetrics and Gynecology, Department of Perinatology, University Medical Centre Ljubljana, 1000 Ljubljana, Slovenia
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10
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Martins D, Batista A, Mouriño H, Russo S, Esgalhado F, dos Reis CRP, Serrano F, Ortigueira M. Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram. SENSORS (BASEL, SWITZERLAND) 2022; 22:7638. [PMID: 36236736 PMCID: PMC9571637 DOI: 10.3390/s22197638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (p-value = 1.31 × 10-12).
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Affiliation(s)
- Daniela Martins
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Arnaldo Batista
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
- UNINOVA-CTS, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Helena Mouriño
- Faculty of Sciences, Lisbon University, Campo Grande, 1749-016 Lisbon, Portugal
- CEAUL Faculty of Sciences, Lisbon University, Campo Grande, 1749-016 Lisbon, Portugal
| | - Sara Russo
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Filipa Esgalhado
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Catarina R. Palma dos Reis
- Department of Obstetrics, University Central Hospital Lisbon (CHULC), 1169-050 Lisboa, Portugal
- Comprehensive Health Research Centre (CHRC), NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisboa, Portugal
| | - Fátima Serrano
- Department of Obstetrics, University Central Hospital Lisbon (CHULC), 1169-050 Lisboa, Portugal
- Comprehensive Health Research Centre (CHRC), NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisboa, Portugal
| | - Manuel Ortigueira
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
- UNINOVA-CTS, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
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Garrett AS, Means SA, Roesler MW, Miller KJW, Cheng LK, Clark AR. Modeling and experimental approaches for elucidating multi-scale uterine smooth muscle electro- and mechano-physiology: A review. Front Physiol 2022; 13:1017649. [PMID: 36277190 PMCID: PMC9585314 DOI: 10.3389/fphys.2022.1017649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
The uterus provides protection and nourishment (via its blood supply) to a developing fetus, and contracts to deliver the baby at an appropriate time, thereby having a critical contribution to the life of every human. However, despite this vital role, it is an under-investigated organ, and gaps remain in our understanding of how contractions are initiated or coordinated. The uterus is a smooth muscle organ that undergoes variations in its contractile function in response to hormonal fluctuations, the extreme instance of this being during pregnancy and labor. Researchers typically use various approaches to studying this organ, such as experiments on uterine muscle cells, tissue samples, or the intact organ, or the employment of mathematical models to simulate the electrical, mechanical and ionic activity. The complexity exhibited in the coordinated contractions of the uterus remains a challenge to understand, requiring coordinated solutions from different research fields. This review investigates differences in the underlying physiology between human and common animal models utilized in experiments, and the experimental interventions and computational models used to assess uterine function. We look to a future of hybrid experimental interventions and modeling techniques that could be employed to improve the understanding of the mechanisms enabling the healthy function of the uterus.
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12
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Albaladejo-Belmonte M, Prats-Boluda G, Ye Lin Y, Garfield RE, Garcia-Casado J. Uterine slow wave: directionality and changes with imminent delivery. Physiol Meas 2022; 43. [PMID: 35896091 DOI: 10.1088/1361-6579/ac84c0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/27/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The slow wave (SW) of the electrohysterogram (EHG) may contain relevant information on the electrophysiological condition of the uterus throughout pregnancy and labor. Our aim was to assess differences in the SW as regards the imminence of labor and the directionality of uterine myoelectrical activity. APPROACH The SW of the EHG was extracted from the signals of the Icelandic 16-electrode EHG database in the bandwidth [5, 30] mHz and its power, spectral content, complexity and synchronization between the horizontal (X) and vertical (Y) directions were characterized by the root mean square (RMS), dominant frequency (domF), sample entropy (SampEn) and maximum cross-correlation (CCmax) of the signals, respectively. Significant differences between parameters at time-to-delivery (TTD) ≤7 vs. >7 days and between the horizontal vs. vertical directions were assessed. MAIN RESULTS The SW power significantly increased in both directions as labor approached (TTD≤7d vs. >7d (mean±SD): x= 0.12±0.10 vs. 0.08±0.06mV; y= 0.12±0.09 vs. 0.08±0.05mV), as well as the dominant frequency in the horizontal direction (= 9.1±1.3 vs. 8.5±1.2mHz) and the synchronization between both directions (= 0.44±0.16 vs. 0.36±0.14). Furthermore, its complexity decreased in the vertical direction (= 6.13·10-2±8.7·10-3 vs. 6.50·10-2±8.3·10-3), suggesting a higher cell-to-cell electrical coupling. Whereas there were no differences between the SW features in both directions in the general population, statistically significant differences were obtained between them in individuals in many cases. SIGNIFICANCE Our results suggest that the SW of the EHG is related to bioelectrical events in the uterus and could provide objective information to clinicians in challenging obstetric scenarios.
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Affiliation(s)
- Monica Albaladejo-Belmonte
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
| | - Yiyao Ye Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
| | - Robert Edward Garfield
- The University of Arizona College of Medicine Tucson, 1501 N Campbell Ave, Tucson, AZ 85724, USA, Tucson, Arizona, 85724-5018, UNITED STATES
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
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13
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Mhajna M, Sadeh B, Yagel S, Sohn C, Schwartz N, Warsof S, Zahar Y, Reches A. A Novel, Cardiac-Derived Algorithm for Uterine Activity Monitoring in a Wearable Remote Device. Front Bioeng Biotechnol 2022; 10:933612. [PMID: 35928952 PMCID: PMC9343786 DOI: 10.3389/fbioe.2022.933612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)’s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent need to develop telehealth tools enabling patients to remotely access care. Here, we describe and demonstrate a novel algorithm enabling remote, non-invasive detection and monitoring of UA by analyzing the modulation of the maternal electrocardiographic and phonocardiographic signals. The algorithm was designed and implemented as part of a wireless, FDA-cleared device designed for remote pregnancy monitoring. Two separate prospective, comparative, open-label, multi-center studies were conducted to test this algorithm.Methods: In the intrapartum study, 41 laboring women were simultaneously monitored with IUPC and the remote pregnancy monitoring device. Ten patients were also monitored with TOCO. In the antepartum study, 147 pregnant women were simultaneously monitored with TOCO and the remote pregnancy monitoring device.Results: In the intrapartum study, the remote pregnancy monitoring device and TOCO had sensitivities of 89.8 and 38.5%, respectively, and false discovery rates (FDRs) of 8.6 and 1.9%, respectively. In the antepartum study, a direct comparison of the remote pregnancy monitoring device to TOCO yielded a sensitivity of 94% and FDR of 31.1%. This high FDR is likely related to the low sensitivity of TOCO.Conclusion: UA monitoring via the new algorithm embedded in the remote pregnancy monitoring device is accurate and reliable and more precise than TOCO standard of care. Together with the previously reported remote fetal heart rate monitoring capabilities, this novel method for UA detection expands the remote pregnancy monitoring device’s capabilities to include surveillance, such as non-stress tests, greatly benefiting women and providers seeking telehealth solutions for pregnancy care.
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Affiliation(s)
- Muhammad Mhajna
- Nuvo-Group, Ltd, Tel-Aviv, Israel
- *Correspondence: Muhammad Mhajna,
| | | | - Simcha Yagel
- Department of Obstetrics and Gynecology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Christof Sohn
- Department of Obstetrics and Gynecology, University Hospital, Heidelberg, Germany
| | - Nadav Schwartz
- Maternal and Child Health Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Steven Warsof
- Ob-Gyn/MFM at Eastern Virginia Medical School, Norfolk, VA, United States
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Nieto-del-Amor F, Prats-Boluda G, Garcia-Casado J, Diaz-Martinez A, Diago-Almela VJ, Monfort-Ortiz R, Hao D, Ye-Lin Y. Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data. SENSORS 2022; 22:s22145098. [PMID: 35890778 PMCID: PMC9319575 DOI: 10.3390/s22145098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 02/01/2023]
Abstract
Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.
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Affiliation(s)
- Félix Nieto-del-Amor
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
- Correspondence:
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
| | - Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
| | | | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain; (V.J.D.-A.); (R.M.-O.)
| | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China;
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
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Almeida M, Mouriño H, Batista AG, Russo S, Esgalhado F, dos Reis CRP, Serrano F, Ortigueira M. Electrohysterography extracted features dependency on anthropometric and pregnancy factors. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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16
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Zhang Y, Hao D, Yang L, Zhou X, Ye-Lin Y, Yang Y. Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors. SENSORS (BASEL, SWITZERLAND) 2022; 22:3352. [PMID: 35591042 PMCID: PMC9104769 DOI: 10.3390/s22093352] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/12/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.
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Affiliation(s)
- Yajun Zhang
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China; (Y.Z.); (L.Y.); (Y.Y.)
| | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China; (Y.Z.); (L.Y.); (Y.Y.)
| | - Lin Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China; (Y.Z.); (L.Y.); (Y.Y.)
| | - Xiya Zhou
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing 100730, China;
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Yimin Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China; (Y.Z.); (L.Y.); (Y.Y.)
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Xu J, Wang M, Zhang J, Chen Z, Huang W, Shen G, Zhang M. Network theory based EHG signal analysis and its application in preterm prediction. IEEE J Biomed Health Inform 2022; 26:2876-2887. [PMID: 34986107 DOI: 10.1109/jbhi.2022.3140427] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk patients followed by medical interventions is essential to the prevention of preterm birth. Based on the relationship between uterine contraction and the fundamental electrical activities of muscles, we extracted effective features from EHG signals recorded from pregnant women, and use them to train classifiers with the purpose of providing high precision in classifying term and preterm pregnancies. METHODS To characterize changes from irregularity to coherence of the uterine activity during the whole pregnancy, network representations of the original electrohysterogram (EHG) signals are established by applying the Horizontal Visibility Graph (HVG) algorithm, from which we extract network degree density and distribution, clustering coefficient and assortativity coefficient. Concerns on the interferences of different noise sources embedded in the EHG signal, we apply Short-Time Fourier Transform (STFT) to expand the original signal in the time-frequency domain. This allows a network representation and the extraction of related features on each frequency component. Feature selection algorithms are then used to filter out unrelated frequency components. We further apply the proposed feature extraction method to EHG signals available in the Term-Preterm EHG database (TPEHG), and use them to train classifiers. We adopt the Partition-Synthesis scheme which splits the original imbalanced dataset into two sets and synthesizes artificial samples separately within each subset to solve the problem of dataset imbalance. RESULTS The optimally selected network-based features, not only contribute to the identification of the essential frequency components of uterine activities related to preterm birth, but also to improved performance in classifying term/preterm pregnancies, i.e., the SVM (Support Vector Machine) classifier trained with the available samples in the TPEHG gives sensitivity, specificity, overall accuracy, and auc values as high as 0.89, 0.93, 0.91, and 0.97, respectively.
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18
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19
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Nsugbe E. A cybernetic framework for predicting preterm and enhancing care strategies: A review. BIOMEDICAL ENGINEERING ADVANCES 2021. [DOI: 10.1016/j.bea.2021.100024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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20
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Nichting TJ, Frenken MWE, van der Woude DAA, van Oostrum NHM, de Vet CM, van Willigen BG, van Laar JOEH, van der Ven M, Oei SG. Non-invasive fetal electrocardiography, electrohysterography and speckle-tracking echocardiography in the second trimester: study protocol of a longitudinal prospective cohort study (BEATS-study). BMC Pregnancy Childbirth 2021; 21:791. [PMID: 34823483 PMCID: PMC8613985 DOI: 10.1186/s12884-021-04265-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/10/2021] [Indexed: 11/20/2022] Open
Abstract
Background Worldwide, hypertensive disorders of pregnancy (HDP), fetal growth restriction (FGR) and preterm birth remain the leading causes of maternal and fetal pregnancy-related mortality and (long-term) morbidity. Fetal cardiac deformation changes can be the first sign of placental dysfunction, which is associated with HDP, FGR and preterm birth. In addition, preterm birth is likely associated with changes in electrical activity across the uterine muscle. Therefore, fetal cardiac function and uterine activity can be used for the early detection of these complications in pregnancy. Fetal cardiac function and uterine activity can be assessed by two-dimensional speckle-tracking echocardiography (2D-STE), non-invasive fetal electrocardiography (NI-fECG), and electrohysterography (EHG). This study aims to generate reference values for 2D-STE, NI-fECG and EHG parameters during the second trimester of pregnancy and to investigate the diagnostic potential of these parameters in the early detection of HDP, FGR and preterm birth. Methods In this longitudinal prospective cohort study, eligible women will be recruited from a tertiary care hospital and a primary midwifery practice. In total, 594 initially healthy pregnant women with an uncomplicated singleton pregnancy will be included. Recordings of NI-fECG and EHG will be made weekly from 22 until 28 weeks of gestation and 2D-STE measurements will be performed 4-weekly at 16, 20, 24 and 28 weeks gestational age. Retrospectively, pregnancies complicated with pregnancy-related diseases will be excluded from the cohort. Reference values for 2D-STE, NI-fECG and EHG parameters will be assessed in uncomplicated pregnancies. After, 2D-STE, NI-fCG and EHG parameters measured during gestation in complicated pregnancies will be compared with these reference values. Discussion This will be the a large prospective study investigating new technologies that could potentially have a high impact on antepartum fetal monitoring. Trial registration Registered on 26 March 2020 in the Dutch Trial Register (NL8769) via https://www.trialregister.nl/trials and registered on 21 October 2020 to the Central Committee on Research Involving Human Subjects (NL73607.015.20) via https://www.toetsingonline.nl/to/ccmo_search.nsf/Searchform?OpenForm.
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Affiliation(s)
- T J Nichting
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands. .,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands. .,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.
| | - M W E Frenken
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - D A A van der Woude
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - N H M van Oostrum
- Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Department of Gynaecology and Obstetrics, University Hospital Gent, 9000, Gent, Belgium
| | - C M de Vet
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - B G van Willigen
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - J O E H van Laar
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - M van der Ven
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - S G Oei
- Department of Gynaecology and Obstetrics, Máxima MC, P.O. Box 7777, 5500 MB, Veldhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.,Eindhoven MedTech Innovation Centre, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
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21
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Cumulative life stressors and stress response to threatened preterm labour as birth date predictors. Arch Gynecol Obstet 2021; 305:1421-1429. [PMID: 34549310 DOI: 10.1007/s00404-021-06251-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/01/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE Preterm birth represents one of the main causes of neonatal morbimortality and a risk factor for neurodevelopmental disorders. Appropriate predictive methods for preterm birth outcome, which consequently would facilitate prevention programs, are needed. We aim to predict birth date in women with a threatened preterm labour (TPL) based on stress response to TPL diagnosis, cumulative life stressors, and relevant obstetric variables. METHODS A prospective cohort of 157 pregnant women with TPL diagnosis between 24 and 31 weeks gestation formed the study sample. To estimate the stress response to TPL, maternal salivary cortisol, α-amylase levels, along with anxiety and depression symptoms were measured. To determine cumulative life stressors, previous traumas, social support, and family functioning were registered. Then, linear regression models were used to examine the effect of potential predictors of birth date. RESULTS Lower family adaptation, higher Body Mass Index (BMI), higher cortisol levels and TPL diagnosis week were the main predictors of birth date. Gestational week at TPL diagnosis showed a non-linear interaction with cortisol levels: TPL women with middle- and high-cortisol levels before 29 weeks of gestation went into imminent labour. CONCLUSION A combination of stress response to TPL diagnosis (salivary cortisol) and cumulative life stressors (family adaptation) together with obstetric factors (TPL gestational week and BMI) was the best birth date predictor. Therefore, a psychosocial therapeutic intervention program aimed to increase family adaptation and decrease cortisol levels at TPL diagnosis as well as losing weight, may prevent preterm birth in symptomatic women.
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Nieto-del-Amor F, Beskhani R, Ye-Lin Y, Garcia-Casado J, Diaz-Martinez A, Monfort-Ortiz R, Diago-Almela VJ, Hao D, Prats-Boluda G. Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. SENSORS 2021; 21:s21186071. [PMID: 34577278 PMCID: PMC8471282 DOI: 10.3390/s21186071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/06/2021] [Accepted: 09/08/2021] [Indexed: 11/16/2022]
Abstract
One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.
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Affiliation(s)
- Félix Nieto-del-Amor
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Raja Beskhani
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
- Correspondence:
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain; (R.M.-O.); (V.J.D.-A.)
| | | | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China;
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
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Nsugbe E, Samuel OW, Sanusi I, Asogbon MG, Li G. A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods. IET CYBER-SYSTEMS AND ROBOTICS 2021. [DOI: 10.1049/csy2.12031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Affiliation(s)
| | | | - Ibrahim Sanusi
- Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK
| | | | - Guanglin Li
- Nsugbe Research Labs Swindon UK
- Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
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Russo S, Batista A, Esgalhado F, Palma dos Reis CR, Serrano F, Vassilenko V, Ortigueira M. Alvarez waves in pregnancy: a comprehensive review. Biophys Rev 2021; 13:563-574. [PMID: 34471439 PMCID: PMC8355272 DOI: 10.1007/s12551-021-00818-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022] Open
Abstract
Alvarez waves are local rhythmic contractions of the myometrium with high frequency and low intensity. They can be detected using internal or external tocography and electrohysterography. Some researchers correlate these small contractions with the initiation of labor, since they have been described as a pattern representing the uterine response to prostaglandin production. Other authors either do not validate a causality relation between Alvarez waves and labor or suggest that they have low predictive value for preterm labor. Alvarez waves' research has become a multidisciplinary subject with inputs ranging from medical science, biomedical engineering, and related areas. A comprehensive review is herein conducted to summarize the state of the art regarding Alvarez waves and their role in the initiation of labor, namely in preterm birth. The results show that a large number of studies have analyzed and characterized Alvarez waves without necessarily digging into their relationship with labor. Publications were categorized in three groups: (A) reports about morphology and characterization of Alvarez waves; (B) publications reporting a positive causality relation between Alvarez waves and labor; and (C) publications reporting an absence of causality regarding the previous hypothesis. Studies in group B outnumbered those in group C. A critical analysis is presented.
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Affiliation(s)
- Sara Russo
- Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
| | - Arnaldo Batista
- Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
- UNINOVA, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
| | - Filipa Esgalhado
- Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
- NMT, S.A., Parque Tecnológico de Cantanhede, Núcleo 04, Lote 3, 3060 -, 197 Cantanhede, Portugal
| | - Catarina R. Palma dos Reis
- Maternidade Alfredo da Costa, Rua Viriato 1, 1050-170 Lisboa, Portugal
- Nova Medical School / Faculty of Medical Sciences, Universidade Nova de Lisboa, 1169-056 Lisboa, Portugal
| | - Fátima Serrano
- Maternidade Alfredo da Costa, Rua Viriato 1, 1050-170 Lisboa, Portugal
- Nova Medical School / Faculty of Medical Sciences, Universidade Nova de Lisboa, 1169-056 Lisboa, Portugal
| | - Valentina Vassilenko
- Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
- NMT, S.A., Parque Tecnológico de Cantanhede, Núcleo 04, Lote 3, 3060 -, 197 Cantanhede, Portugal
| | - Manuel Ortigueira
- Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
- UNINOVA, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal
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Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography. SENSORS 2021; 21:s21103350. [PMID: 34065847 PMCID: PMC8151582 DOI: 10.3390/s21103350] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/23/2021] [Accepted: 05/07/2021] [Indexed: 11/17/2022]
Abstract
Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.
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Song X, Qiao X, Hao D, Yang L, Zhou X, Xu Y, Zheng D. Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate. Sci Rep 2021; 11:1956. [PMID: 33479344 PMCID: PMC7820321 DOI: 10.1038/s41598-021-81492-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/14/2020] [Indexed: 11/09/2022] Open
Abstract
Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.
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Affiliation(s)
- Xiaoxiao Song
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China
| | - Xiangyun Qiao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China
| | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China.
| | - Lin Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China
| | - Xiya Zhou
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yuhang Xu
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Priory Street, Coventry, CV1 5FB, UK
| | - Dingchang Zheng
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Priory Street, Coventry, CV1 5FB, UK
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Vinothini S, Punitha N, Karthick P, Ramakrishnan S. Automated detection of preterm condition using uterine electromyography based topological features. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records. SENSORS 2020; 20:s20247328. [PMID: 33419319 PMCID: PMC7766070 DOI: 10.3390/s20247328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 11/16/2022]
Abstract
The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term–Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features—the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz—showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.
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Garfield RE, Murphy L, Gray K, Towe B. Review and Study of Uterine Bioelectrical Waveforms and Vector Analysis to Identify Electrical and Mechanosensitive Transduction Control Mechanisms During Labor in Pregnant Patients. Reprod Sci 2020; 28:838-856. [PMID: 33090378 DOI: 10.1007/s43032-020-00358-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/11/2020] [Indexed: 12/15/2022]
Abstract
The bioelectrical signals that produce uterine contractions during parturition are not completely understood. The objectives are as follows: (1) to review the literature and information concerning uterine biopotential waveforms generated by the uterus, known to produce contractions, and evaluate mechanotransduction in pregnant patients using electromyographic (EMG) recording methods and (2) to study a new approach, uterine vector analysis, commonly used for the heart: vectorcardiography analysis. The patients used in this study were as follows: (1) patients at term not in labor (n = 3); (2) patients during the 1st stage of labor at cervical dilations from 2 to 10 cm (n = 30); and (3) patients in the 2nd stage of labor and during delivery (n = 3). We used DC-coupled electrodes and PowerLab hardware (model no. PL2604, ADInstruments, Castle Hill, Australia), with software (LabChart, ADInstruments) for storage and analysis of biopotentials. Uterine and abdominal EMG recordings were made from the surface of each patient using 3 electrode pairs with 1 pair (+ and -, with a 31-cm spacing distance) placed in the right/left position (X position) and with 1 pair placed in an up/down position (Y position, also 31 cm apart) and with the third pair at the front/back (Z position). Using signals from the three X, Y, and Z electrodes, slow (0.03 to 0.1 Hz, high amplitude) and fast wave (0.3 to 1 Hz, low amplitude) biopotentials were recorded. The amplitudes of the slow waves and fast waves were significantly higher during the 2nd stage of labor compared to the 1st stage (respectively, p = 9.54 × e-3 and p = 3.94 × e-7). When 2 channels were used, for example, the X vs. Y, for 2-D vector analysis or 3 channels, X vs. Y vs. Z, for 3-D analysis, are plotted against each other on their axes, this produces a vector electromyometriogram (EMMG) that shows no directionality for fast waves and a downward direction for slow waves. Similarly, during the 2nd stage of labor during abdominal contractions ("pushing"), the slow and fast waves were enlarged. Manual applied pressure was used to evoke bioelectrical activity to examine the mechanosensitivity of the uterus. Conclusions: (1) Phasic contractility of the uterus is a product of slow waves and groups of fast waves (bursts of spikes) to produce myometrial contractile responses. (2) 2-D and 3-D uterine vector analyses (uterine vector electromyometriogram) demonstrate no directionality of small fast waves while the larger slow waves represent the downward direction of biopotentials towards the cervical opening. (3) Myometrial cell action event excitability and subsequent contractility likely amplify slow wave activity input and uterine muscle contractility via mechanotransduction systems. (4) Models illustrate the possible relationships of slow to fast waves and the association of a mechanotransduction system and pacemaker activity as observed for slow waves and pacemakers in gastrointestinal muscle. (5) The interaction of these systems is thought to regulate uterine contractility. (6) This study suggests a potential indicator of delivery time. Such vector approaches might help us predict the progress of gestation and better estimate the timing of delivery, gestational pathologies reflected in bioelectric events, and perhaps the potential for premature delivery drug and mechanical interventions.
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Affiliation(s)
- R E Garfield
- Department of Obstetrics and Gynecology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA.
| | - Lauren Murphy
- Department of Obstetrics and Gynecology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
| | - Kendra Gray
- Department of Obstetrics and Gynecology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
| | - Bruce Towe
- Department of Biomedical Engineering, Arizona State University, Tempe, AZ, USA
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Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition. ENTROPY 2020; 22:e22080798. [PMID: 33286569 PMCID: PMC7517370 DOI: 10.3390/e22080798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/08/2020] [Accepted: 07/11/2020] [Indexed: 12/14/2022]
Abstract
Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed to analyze the nonlinear features of raw electrohysterogram (EHG) time series collected from women at the third trimester of pregnancy (TT) and later during term active parturition (P) by PhEn. We collected 10-min longitudinal transabdominal recordings of 24 low-risk pregnant women at TT (from 35 to 38 weeks of pregnancy) and P (>39 weeks of pregnancy). We computed the second-order difference plots (SODPs) for the TT and P stages, and we evaluated the PhEn by modifying the k value, a coarse-graining parameter. Our results pointed out that PhEn in TT is characterized by a higher likelihood of manifesting nonlinear dynamics compared to the P condition. However, both conditions maintain percentages of nonlinear series higher than 66%. We conclude that the nonlinear features appear to be retained for both stages of pregnancy despite the uterine and cervical reorganization process that occurs in the transition from the third trimester to parturition.
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Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios. ENTROPY 2020; 22:e22070743. [PMID: 33286515 PMCID: PMC7517284 DOI: 10.3390/e22070743] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/18/2020] [Accepted: 07/03/2020] [Indexed: 12/19/2022]
Abstract
Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel–Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.
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Spatial-dependent regularization to solve the inverse problem in electromyometrial imaging. Med Biol Eng Comput 2020; 58:1651-1665. [PMID: 32458384 DOI: 10.1007/s11517-020-02183-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 04/30/2020] [Indexed: 10/24/2022]
Abstract
Recently, electromyometrial imaging (EMMI) was developed to non-invasively image uterine contractions in three dimensions. EMMI collects body surface electromyography (EMG) measurements and uses patient-specific body-uterus geometry generated from magnetic resonance images to reconstruct uterine electrical activity. Currently, EMMI uses the zero-order Tikhonov method with mean composite residual and smoothing operator (CRESO) to stabilize the underlying ill-posed inverse computation. However, this method is empirical and implements a global regularization parameter over all uterine sites, which is sub-optimal for EMMI given the severe eccentricity of body-uterus geometry. To address this limitation, we developed a spatial-dependent (SP) regularization method that considers both body-uterus eccentricity and EMG noise. We used electrical signals simulated with spherical and realistic geometry models to compare the reconstruction accuracy of the SP method to those of the CRESO and the L-Curve methods. The SP method reconstructed electrograms and potential maps more accurately than the other methods, especially in cases of high eccentricity and noise contamination. Thus, the SP method should facilitate clinical use of EMMI and can be used to improve the accuracy of other electrical imaging modalities, such as Electrocardiographic Imaging. Graphical abstract The spatial-dependent regularization (SP) technique was designed to improve the accuracy of Electromyometrial Imaging (EMMI). The top panel shows the eccentricity of body-uterus geometry and four representative body surface electrograms. The bottom panel shows boxplots of correlation coefficients and relative errors for the electrograms reconstructed with SP and two conventional methods, the L-Curve and mean CRESO methods.
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Diaz-Martinez A, Mas-Cabo J, Prats-Boluda G, Garcia-Casado J, Cardona-Urrego K, Monfort-Ortiz R, Lopez-Corral A, De Arriba-Garcia M, Perales A, Ye-Lin Y. A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity. SENSORS 2020; 20:s20113023. [PMID: 32466584 PMCID: PMC7308960 DOI: 10.3390/s20113023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/04/2020] [Accepted: 05/23/2020] [Indexed: 11/16/2022]
Abstract
Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.
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Affiliation(s)
- Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Javier Mas-Cabo
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Karen Cardona-Urrego
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Angel Lopez-Corral
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Maria De Arriba-Garcia
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Alfredo Perales
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
- Correspondence: ; Tel.: +34-96-387-70-00 (ext. 76026)
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Sichitiu J, Vial Y, Panchaud A, Baud D, Desseauve D. Tachysystole and risk of cesarean section after labor induction using misoprostol: A cohort study. Eur J Obstet Gynecol Reprod Biol 2020; 249:54-58. [PMID: 32361173 DOI: 10.1016/j.ejogrb.2020.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 04/04/2020] [Accepted: 04/09/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVES To investigate if tachysystole was associated with an increased risk of cesarean section or unfavorable maternal or neonatal outcomes following induction of labor by misoprostol vaginal inserts. STUDY DESIGN We conducted a retrospective cohort study of 446 women over 37 weeks of gestation admitted for labor induction by misoprostol vaginal inserts between May 2016 and May 2017. Fetal heart rate and uterine activity tracings were assessed for tachysystole, defined as ≥ 6 contractions per 10 min, averaged over a 30-minute window. Univariate analysis was performed by using t-test and Chi-square, comparing demographics, pregnancy characteristics, intrapartum monitoring, mode of delivery, neonatal outcomes (Apgar score < 7 at 5 min, umbilical cord artery pH < 7.10, neonatal intensive care unit admission) and maternal outcomes, with regard to the presence of tachysystole. The association between tachysystole and cesarean section was evaluated after adjusting for potential confounders by a modified Poisson regression model, expressed as an adjusted risk ratio and 95 % confidence intervals. RESULTS A total of 140 women (31.4 %) presented with tachysystole. The median duration of tachysystole was 2 h 12 min. The rate of cesarean section was 25.0 % (N = 35) among patients with tachysystole and 19.6 % (N = 60) for those without tachysystole. Presence of tachysystole during induction of labor with misoprostol vaginal inserts was not associated with cesarean section (adjusted risk ratio,1.0; 95 % confidence interval, 0.7-1.4). Neonatal and maternal outcomes were similar between mothers who did and did not experience tachysystole. CONCLUSIONS This study illustrates that tachysystole is not associated with an increased risk of cesarean section after induction of labor by misoprostol vaginal inserts. The impact of excessive uterine activity on the fetal wellbeing defined by the frequency of uterine contraction alone is probably insufficient. Further research on the development of accurate measures of uterine contractility is necessary to better understand its effect on fetal well-being.
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Affiliation(s)
- Joanna Sichitiu
- Women - Mother - Child Department, University Hospital of Lausanne, Lausanne, Switzerland.
| | - Yvan Vial
- Women - Mother - Child Department, University Hospital of Lausanne, Lausanne, Switzerland
| | - Alice Panchaud
- School of Pharmaceutical Sciences, Geneva University and Service of Pharmacy, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Baud
- Women - Mother - Child Department, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Desseauve
- Women - Mother - Child Department, University Hospital of Lausanne, Lausanne, Switzerland
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Degbedzui DK, Yüksel ME. Accurate diagnosis of term-preterm births by spectral analysis of electrohysterography signals. Comput Biol Med 2020; 119:103677. [PMID: 32339119 DOI: 10.1016/j.compbiomed.2020.103677] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/23/2020] [Accepted: 02/23/2020] [Indexed: 12/20/2022]
Abstract
Preterm delivery contributes to an increased risk of fetal and maternal death as well as several health deficiencies, thereby requiring special care and treatment that result in high financial costs. It is therefore of key importance to diagnose preterm delivery in advance in order to avoid or minimize its undesirable consequences. This paper proposes a novel method for non-invasive diagnosis of preterm delivery based on the classification of electrohysterography (EHG) signals. First, the EHG signal, which is related to the electrical activity of uterine muscles is recorded from the maternal fundus using surface electrodes. Then, the signal is sliced into frames for spectral analysis. Next, spectral analyses of the individual EHG signal frames are carried out and centroid frequencies of the frames are computed, establishing the elements of a feature vector that represents the time-varying spectral content of the EHG signal. Finally, this feature vector is employed for the classification of the underlying EHG signal for term-preterm diagnosis. The efficiency of the proposed approach is evaluated and compared with representative methods from the literature. Our results demonstrate that the proposed approach exhibits superior performance over other methods.
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Affiliation(s)
- Derek Kweku Degbedzui
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, 38039, Turkey.
| | - Mehmet Emin Yüksel
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, 38039, Turkey.
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Garfield RE, Lucovnik M, Chambliss L, Qian X. Monitoring the onset and progress of labor with electromyography in pregnant women. CURRENT OPINION IN PHYSIOLOGY 2020. [DOI: 10.1016/j.cophys.2019.10.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Hao D, Qiao X, Song X, Wang Y, Qiu Q, Jiang H, Chen F. Estimation of 8-Electrode Configuration for Recognition of Uterine Contraction with Electrohysterogram. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:672-675. [PMID: 31945987 DOI: 10.1109/embc.2019.8857389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
As the representative of electrical activity from uterine muscle, electrohysterogram (EHG) is recorded non-invasively by multiple electrodes positioned on the abdominal surface. The purpose of our paper is to estimate different electrode configurations for recognizing uterine contractions (UCs) with EHG signals. 8-electrode configuration was taken as an example to show our novel method with convolutional neural network (CNN) classification and score. The open accessed Icelandic 16-electrode EHG database was adopted in our study. With 8-electrode configuration, EHG signals corresponding to UCs and non-UCs were segmented and saved as image patches. The CNN was established and trained by thousands of EHG segments. The performance of CNN was evaluated by the area under curve (AUC) and accuracy of recognizing UCs and non-UCs. Seven different 8-electrode configurations were scored and ranked. It was found the 8-electrode configuration with 4 on the uterine fundus, 2 on the body and 2 on the cervix achieved the AUC of 0.766 and the highest score of 2.197. Among the configurations we have tried, it is concluded that the 8 electrodes in 4-2-2 configuration placed along the uterus as an upside-down pear could provide the most important information for recognition of UC based on our experiments.
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Lee KJ, Yoo J, Kim YH, Kim SH, Kim SC, Kim YH, Kwak DW, Kil K, Park MH, Park H, Shim JY, Son GH, Lee KA, Oh SY, Oh KJ, Cho GJ, Shim SY, Cho SJ, Cho HY, Cha HH, Choi SK, Hwang JY, Hwang HS, Kwon EJ, Kim YJ. The Clinical Usefulness of Predictive Models for Preterm Birth with Potential Benefits: A KOrean Preterm collaboratE Network (KOPEN) Registry-Linked Data-Based Cohort Study. Int J Med Sci 2020; 17:1-12. [PMID: 31929733 PMCID: PMC6945556 DOI: 10.7150/ijms.37626] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 10/25/2019] [Indexed: 11/05/2022] Open
Abstract
Background: Preterm birth is strongly associated with increasing mortality, incidence of disability, intensity of neonatal care required, and consequent costs. We examined the clinical utility of the potential preterm birth risk factors from admitted pregnant women with symptomatic preterm labor and developed prediction models to obtain information for prolonging pregnancies. Methods: This retrospective study included pregnant women registered with the KOrean Preterm collaboratE Network (KOPEN) who had symptomatic preterm labor, between 16 and 34 gestational weeks, in a tertiary care center from March to November 2016. Demographics, obstetric and medical histories, and basic laboratory test results obtained at admission were evaluated. The preterm birth probability was assessed using a nomogram and decision tree according to birth gestational age: early preterm, before 32 weeks; late preterm, between 32 and 37 weeks; and term, after 37 weeks. Results: Of 879 registered pregnant women, 727 who gave birth at a designated institute were analyzed. The rates of early preterm, late preterm, and term births were 18.16%, 44.02%, and 37.83%, respectively. With the developed nomogram, the concordance index for early and late preterm births was 0.824 (95% CI: 0.785-0.864) and 0.717 (95% CI: 0.675-0.759) respectively. Preterm birth was significantly more likely among women with multiple pregnancy and had water leakage due to premature rupture of membrane. The prediction rate for preterm birth based on decision tree analysis was 86.9% for early preterm and 73.9% for late preterm; the most important nodes are watery leakage for early preterm birth and multiple pregnancy for late preterm birth. Conclusion: This study aims to develop an individual overall probability of preterm birth based on specific risk factors at critical gestational times of preterm birth using a range of clinical variables recorded at the initial hospital admission. Therefore, these models may be useful for clinicians and patients in clinical decision-making and for hospitalization or lifestyle coaching in an outpatient setting.
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Affiliation(s)
- Kyung Ju Lee
- Department of Obstetrics and Gynecology, Korea University Medical Center, Seoul, Korea.,Department of Public Health, Korea University Graduate School, Seoul, Korea
| | - Jinho Yoo
- YooJin BioSoft Co., Ltd, Goyang-si Gyeonggi-do, Korea
| | - Young-Han Kim
- Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Korea
| | - Soo Hyun Kim
- Department of Obstetrics & Gynecology, CHA Gangnam Medical Center, CHA University, Seoul, Korea
| | - Seung Chul Kim
- Department of Obstetrics and Gynecology, Biomedical Research Institute, Pusan National University College of Medicine, Busan, Korea
| | - Yoon Ha Kim
- Department of Obstetrics and Gynecology, Chonnam National University Medical School, Gwangju, Korea
| | - Dong Wook Kwak
- Department of Obstetrics and Gynecology, Cheil General Hospital and Woman's Healthcare Center, Dankook University College of Medicine, Seoul, Korea.,Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Kicheol Kil
- Department of Obstetrics and Gynecology, College of Medicine, Catholic University of Korea, Seoul, Korea
| | - Mi Hye Park
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Hyesook Park
- Department of Preventive Medicine, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Jae-Yoon Shim
- Department of Obstetrics & Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ga Hyun Son
- Department of Obstetrics and Gynecology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Kyung A Lee
- Department of Obstetrics and Gynecology, Kyung Hee University School of Medicine, Seoul, Korea
| | - Soo-Young Oh
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Kyung Joon Oh
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Geum Joon Cho
- Department of Obstetrics and Gynecology, Korea University Medical Center, Seoul, Korea
| | - So-Yeon Shim
- Department of Pediatrics, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Su Jin Cho
- Department of Pediatrics, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Hee Young Cho
- Department of Obstetrics and Gynecology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Korea
| | - Hyun-Hwa Cha
- Department of Obstetrics & Gynecology, Kyungpook National University Hospital, Kyungpook National University, School of Medicine, Daegu, Korea
| | - Sae Kyung Choi
- Department of Obstetrics and Gynecology, College of Medicine, Catholic University of Korea, Seoul, Korea
| | - Jong Yun Hwang
- Department of Obstetrics and Gynecology, Kangwon National University School of Medicine, Kangwon-do, Korea
| | - Han-Sung Hwang
- Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea
| | - Eun Jin Kwon
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Young Ju Kim
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Womans University, Seoul, Korea
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Wang H, Wu W, Talcott M, McKinstry RC, Woodard PK, Macones GA, Schwartz AL, Cuculich P, Cahill AG, Wang Y. Accuracy of electromyometrial imaging of uterine contractions in clinical environment. Comput Biol Med 2019; 116:103543. [PMID: 31786490 DOI: 10.1016/j.compbiomed.2019.103543] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 11/06/2019] [Accepted: 11/13/2019] [Indexed: 11/24/2022]
Abstract
Clinically, uterine contractions are monitored with tocodynamometers or intrauterine pressure catheters. In the research setting, electromyography (EMG), which detects electrical activity of the uterus from a few electrodes on the abdomen, is feasible, can provide more accurate data than these other methods, and may be useful for predicting preterm birth. However, EMG lacks sufficient spatial resolution and coverage to reveal where uterine contractions originate, how they propagate, and whether preterm contractions differ between women who do and do not progress to preterm delivery. To address those limitations, electromyometrial imaging (EMMI) was recently developed and validated to non-invasively assess three-dimensional (3D) electrical activation patterns on the entire uterine surface in pregnant sheep. EMMI uses magnetic resonance imaging to obtain subject-specific body-uterus geometry and collects uterine EMG data from up to 256 electrodes on the body surface. EMMI software then solves an ill-posed inverse computation to combine the two datasets and generate maps of electrical activity on the entire 3D uterine surface. Here, we assessed the feasibility to clinically translate EMMI by evaluating EMMI's accuracy under the unavoidable geometrical alterations and electrical noise contamination in a clinical environment. We developed a hybrid experimental-simulation platform to model the effects of fetal kicks, contractions, fetal/maternal movements, and noise contamination caused by maternal respiration and environmental electrical activity. Our data indicate that EMMI can accurately image uterine electrical activity in the presence of geometrical deformations and electrical noise, suggesting that EMMI can be reliably translated to non-invasively image 3D uterine electrical activation in pregnant women.
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Affiliation(s)
- Hui Wang
- Department of Physics, Washington University, St. Louis, MO, 63130, USA; Center for Reproductive Health Sciences, Washington University, St. Louis, MO, 63130, USA; Department of Obstetrics & Gynecology, School of Medicine, St. Louis, MO, 63110, USA.
| | - Wenjie Wu
- Center for Reproductive Health Sciences, Washington University, St. Louis, MO, 63130, USA; Department of Obstetrics & Gynecology, School of Medicine, St. Louis, MO, 63110, USA; Department of Biomedical Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Michael Talcott
- Division of Comparative Medicine, Washington University, St. Louis, MO, 63110, USA
| | - Robert C McKinstry
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Pamela K Woodard
- Department of Biomedical Engineering, Washington University, St. Louis, MO, 63130, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - George A Macones
- Department of Women's Health, University of Texas at Austin, Austin, TX, 78712, USA
| | - Alan L Schwartz
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Phillip Cuculich
- Department of Cardiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Alison G Cahill
- Department of Women's Health, University of Texas at Austin, Austin, TX, 78712, USA.
| | - Yong Wang
- Center for Reproductive Health Sciences, Washington University, St. Louis, MO, 63130, USA; Department of Obstetrics & Gynecology, School of Medicine, St. Louis, MO, 63110, USA; Department of Biomedical Engineering, Washington University, St. Louis, MO, 63130, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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40
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Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Guimarães MF, Rabelo FA, Figueiredo I. Knowledge about Neonatal Screening among Postpartum Women and Complexity Level of Birthing Facilities. Int J Neonatal Screen 2019; 5:8. [PMID: 33072968 PMCID: PMC7510197 DOI: 10.3390/ijns5010008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 01/20/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND To ascertain the degree of knowledge of postpartum women about important aspects related to the neonatal screening process and whether differences of opinion exist between those who deliver in low-complexity versus high-complexity health facilities (low-risk versus high-risk pregnancies, respectively). METHODS This was a prospective, cross-sectional, questionnaire-based study. The sample consisted of postpartum women recruited from 2013 to 2015 at public maternity hospitals in the city of Niterói, Brazil. Participants were divided into two groups and completed a questionnaire consisting of Likert-scored items. Continuous variables were analyzed with the Mann-Whitney test, and categorical variables, with Fisher's test. A confirmatory factor analysis of participants' answers was performed. RESULTS Of 188 women enrolled, 54 (28.7%) had incomplete elementary education; 119 (62.2%) had attended more than six antenatal care visits. The mean age was 25.57 years. Nearly all women (n = 179, 95.2%) were roomed-in with their infants. Knowledge of neonatal screening was very similar in the high-complexity and low-complexity groups. Divergences were limited to items regarding the risks of neonatal screening. CONCLUSIONS The degree of knowledge among postpartum women was similar among high- and low-complexity facilities. Those who attended high-complexity facilities had longer hospital stays and greater adherence to ethical issues regarding neonatal screening.
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Affiliation(s)
- Mariana F. Guimarães
- Correspondence: (M.F.G.); (I.F.J.); Tel.: +55-21-982-123-045 or +55-21-2629-9028 (I.F.J.)
| | | | - Israel Figueiredo
- Correspondence: (M.F.G.); (I.F.J.); Tel.: +55-21-982-123-045 or +55-21-2629-9028 (I.F.J.)
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Performance of source imaging techniques of spatially extended generators of uterine activity. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Mas-Cabo J, Ye-Lin Y, Garcia-Casado J, Alberola-Rubio J, Perales A, Prats-Boluda G. Uterine contractile efficiency indexes for labor prediction: A bivariate approach from multichannel electrohysterographic records. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.07.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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