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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] [MESH Headings] [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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Ballit A, Dao TT. Multiphysics and multiscale modeling of uterine contractions: integrating electrical dynamics and soft tissue deformation with fiber orientation. Med Biol Eng Comput 2024; 62:791-816. [PMID: 38008805 DOI: 10.1007/s11517-023-02962-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/28/2023] [Indexed: 11/28/2023]
Abstract
The development of a comprehensive uterine model that seamlessly integrates the intricate interactions between the electrical and mechanical aspects of uterine activity could potentially facilitate the prediction and management of labor complications. Such a model has the potential to enhance our understanding of the initiation and synchronization mechanisms involved in uterine contractions, providing a more profound comprehension of the factors associated with labor complications, including preterm labor. Consequently, it has the capacity to assist in more effective preparation and intervention strategies for managing such complications. In this study, we present a computational model that effectively integrates the electrical and mechanical components of uterine contractions. By combining a state-of-the-art electrical model with the Hyperelastic Mass-Spring Model (HyperMSM), we adopt a multiphysics and multiscale approach to capture the electrical and mechanical activities within the uterus. The electrical model incorporates the generation and propagation of action potentials, while the HyperMSM simulates the mechanical behavior and deformations of the uterine tissue. Notably, our model takes into account the orientation of muscle fibers, ensuring that the simulated contractions align with their inherent directional characteristics. One noteworthy aspect of our contraction model is its novel approach to scaling the rest state of the mesh elements, as opposed to the conventional method of applying mechanical loads. By doing so, we eliminate artificial strain energy resulting from the resistance of soft tissues' elastic properties during contractions. We validated our proposed model through test simulations, demonstrating its feasibility and its ability to reproduce expected contraction patterns across different mesh resolutions and configurations. Moving forward, future research efforts should prioritize the validation of our model using robust clinical data. Additionally, it is crucial to refine the model by incorporating a more realistic uterus model derived from medical imaging. Furthermore, applying the model to simulate the entire childbirth process holds immense potential for gaining deeper insights into the intricate dynamics of labor.
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Affiliation(s)
- Abbass Ballit
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France
| | - Tien-Tuan Dao
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France.
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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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Goldsztejn U, Nehorai A. Estimating uterine activity from electrohysterogram measurements via statistical tensor decomposition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Ghahremani Arekhloo N, Parvizi H, Zuo S, Wang H, Nazarpour K, Marquetand J, Heidari H. Alignment of magnetic sensing and clinical magnetomyography. Front Neurosci 2023; 17:1154572. [PMID: 37274205 PMCID: PMC10232862 DOI: 10.3389/fnins.2023.1154572] [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: 01/30/2023] [Accepted: 04/24/2023] [Indexed: 06/06/2023] Open
Abstract
Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG.
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Affiliation(s)
- Negin Ghahremani Arekhloo
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Hossein Parvizi
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
| | - Siming Zuo
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Huxi Wang
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Kianoush Nazarpour
- Neuranics Ltd., Glasgow, United Kingdom
- School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
| | - Justus Marquetand
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- MEG Centre, University of Tübingen, Tübingen, Germany
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
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Wang H, Wen Z, Wu W, Sun Z, Kisrieva-Ware Z, Lin Y, Wang S, Gao H, Xu H, Zhao P, Wang Q, Macones GA, Schwartz AL, Cuculich P, Cahill AG, Wang Y. Noninvasive electromyometrial imaging of human uterine maturation during term labor. Nat Commun 2023; 14:1198. [PMID: 36918533 PMCID: PMC10015052 DOI: 10.1038/s41467-023-36440-0] [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: 05/13/2022] [Accepted: 01/23/2023] [Indexed: 03/16/2023] Open
Abstract
Electromyometrial imaging (EMMI) was recently developed to image the three-dimensional (3D) uterine electrical activation during contractions noninvasively and accurately in sheep. Herein we describe the development and application of a human EMMI system to image and evaluate 3D uterine electrical activation patterns at high spatial and temporal resolution during human term labor. We demonstrate the successful integration of the human EMMI system during subjects' clinical visits to generate noninvasively the uterine surface electrical potential maps, electrograms, and activation sequence through an inverse solution using up to 192 electrodes distributed around the abdomen surface. Quantitative indices, including the uterine activation curve, are developed and defined to characterize uterine surface contraction patterns. We thus show that the human EMMI system can provide detailed 3D images and quantification of uterine contractions as well as novel insights into the role of human uterine maturation during labor progression.
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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 School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Zichao Wen
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Wenjie Wu
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Zhexian Sun
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Zulfia Kisrieva-Ware
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yiqi Lin
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Sicheng Wang
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Hansong Gao
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Haonan Xu
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Peinan Zhao
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Qing Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - George A Macones
- Department of Women's Health, Dell Medical School, The 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, Dell Medical School, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yong Wang
- Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63130, USA.
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Biomedical Engineering, Washington University, St. Louis, MO, 63130, USA.
- Department of Electrical and Systems 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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Rosen H, Yogev Y. Assessment of uterine contractions in labor and delivery. Am J Obstet Gynecol 2023; 228:S1209-S1221. [PMID: 37164494 DOI: 10.1016/j.ajog.2022.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 09/01/2022] [Accepted: 09/01/2022] [Indexed: 03/21/2023]
Abstract
Normal labor and delivery are dependent on the presence of regular and effective contractions of the uterine myometrium. The mechanisms responsible for the initiation and maintenance of adequate and synchronized uterine activity that are necessary for labor and delivery result from a complex interplay of hormonal, mechanical, and electrical factors that have not yet been fully elucidated. Monitoring uterine activity during term labor and in suspected preterm labor is an important component of obstetrical care because cases of inadequate and excessive uterine activity can be associated with substantial maternal and neonatal morbidity and mortality. Inadequate labor progress is a common challenge encountered in intrapartum care, with labor dystocia being the most common indication for cesarean deliveries performed during labor. Hereafter, an accurate assessment of uterine activity during labor can assist in the management of protracted labor by diagnosing inadequate uterine activity and facilitating the titration of uterotonic medications before a trial of labor is prematurely terminated. Conversely, the ability to diagnose unwanted or excessive uterine activity is also critical in cases of threatened preterm labor, tachysystole, or patients undergoing a trial of labor after cesarean delivery. Knowledge of uterine activity in these cases may guide the use of tocolytic medications or raise suspicion of uterine rupture. Current diagnostic capabilities are less than optimal, hindering the medical management of term and preterm labor. Currently, different methods exist for evaluating uterine activity during labor, including manual palpation, external tocodynamometry, intrauterine pressure monitoring, and electrical uterine myometrial activity tracing. Legacy uterine monitoring techniques have advantages and limitations. External tocodynamometry is the most widespread tool in clinical use owing to its noninvasive nature and its ability to time contractions against the fetal heart rate monitor. However, it does not provide information regarding the strength of uterine contractions and is limited by signal loss with maternal movements. Conversely, the intrauterine pressure catheter quantifies the strength of uterine contractions; however, its use is limited by its invasiveness, risk for complications, and limited additive value in all but few clinical scenarios. New monitoring methods are being used, such as electrical uterine monitoring, which is noninvasive and does not require ruptured membranes. Electrical uterine monitoring has yet to be incorporated into common clinical practice because of lack of access to this technology, its high cost, and the need for appropriate training of clinical staff. Further work needs to be done to increase the accessibility and implementation of this technique by experts, and further research is needed to implement new practical and useful methods. This review describes current clinical tools for uterine activity assessment during labor and discusses their advantages and shortcomings. The review also summarizes current knowledge regarding novel technologies for monitoring uterine contractions that are not yet in widespread use, but are promising and could help improve our understanding of the physiology of labor, delivery, and preterm labor, and ultimately enhance patient care.
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Affiliation(s)
- Hadar Rosen
- Department of Obstetrics and Gynecology, Mayanei Hayeshua Medical Center, Bnei Brak, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Yariv Yogev
- Lis Maternity and Women's Hospital, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Qian X, Zhou B, Li P, Garfield RE, Liu H. Quantitative analysis for grading uterine electromyography activities during labor. Am J Obstet Gynecol MFM 2023; 5:100798. [PMID: 36351529 DOI: 10.1016/j.ajogmf.2022.100798] [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: 08/06/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND The strength of uterine contraction is one of the decisive factors for labor progression and parturition. Clinicians usually encounter difficulties in early identification of inadequate contractions and in oxytocin treatment. Electromyography-an emerging technology for uterine contraction monitoring-can quantify the intensity of myoelectric activity of uterine contraction. Therefore, grading patients with different uterine contraction intensities by electromyography is of great significance to the clinical intensive management of uterine contraction and labor process. OBJECTIVE This study aimed to quantify and grade electromyography activity during the latent phase of the first stage of labor and explore its relationship with oxytocin treatment and length of labor. STUDY DESIGN We performed a retrospective cohort study to identify electromyography parameters as a predictor for oxytocin treatment and length of labor among a cohort of term singleton primipara (n=508) during the latent phase who delivered in Guangzhou between August 2018 and December 2021. The electromyography parameters were graded according to the quartile method, and the significance of grading and delivery outcome was explored. Univariate and multivariate logistic regression were used to determine the predictors of oxytocin treatment. RESULTS Maternal gestational age (adjusted risk ratio, 1.2; 95% confidence interval, 1.0-1.5), root mean square (adjusted risk ratio, 0.01; 95% confidence interval, 0.004-0.03), and power (adjusted risk ratio, 0.02; 95% confidence interval, 0.01-0.05) were significant predictors of oxytocin argumentation. The low electromyography activity group had a longer first stage labor and total labor time and were more likely to use oxytocin. CONCLUSION Electromyography parameters root mean square and power had high predictive values for later oxytocin treatment among patients with spontaneous labor. Patients with low-grade electromyography were more likely need oxytocin treatment. Electromyography grading is very important for its clinical promotion and use, and it could lead to more reliable analyses of oxytocin treatments and eventually to more effective interventions to prevent prolonged labor.
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Affiliation(s)
- Xueya Qian
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University Guangzhou, Guangzhou, China (Drs Qian, Zhou, Li, and Liu)
| | - Bingqian Zhou
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University Guangzhou, Guangzhou, China (Drs Qian, Zhou, Li, and Liu)
| | - Pin Li
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University Guangzhou, Guangzhou, China (Drs Qian, Zhou, Li, and Liu)
| | - Robert E Garfield
- Department of Obstetrics and Gynecology, The University of Arizona College of Medicine-Phoenix, Phoenix, AZ (Dr Garfield)
| | - Huishu Liu
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University Guangzhou, Guangzhou, China (Drs Qian, Zhou, Li, and Liu).
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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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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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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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12
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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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Xu Y, Hao D, Taggart MJ, Zheng D. Regional identification of information flow termination of electrohysterographic signals: Towards understanding human uterine electrical propagation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106967. [PMID: 35763875 DOI: 10.1016/j.cmpb.2022.106967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The uterine electrohysterogram (EHG) contains important information about electrical signal propagation which may be useful to monitor and predict the progress of pregnancy towards parturition. Directed information processing has the potential to be of use in studying EHG recordings. However, so far, there is no directed information-based estimation scheme that has been applied to investigating the propagation of human EHG recordings. To realize this, the approach of directed information and its reliability and adaptability should be scientifically studied. METHODS We demonstrated an estimation scheme of directed information to identify the spatiotemporal relationship between the recording channels of EHG signal and assess the algorithm reliability initially using simulated data. Further, a regional identification of information flow termination (RIIFT) approach was developed and applied for the first time to extant multichannel EHG signals to reveal the terminal zone of propagation of the electrical activity associated with uterine contraction. RIIFT operates by estimating the pairwise directed information between neighboring EHG channels and identifying the location where there is the strongest inward flow of information. The method was then applied to publicly-available experimental data obtained from pregnant women with the use of electrodes arranged in a 4-by-4 grid. RESULTS Our results are consistent with the suggestions from the previous studies with the added identification of preferential sites of excitation termination - within the estimated area, the direction of surface action potential propagation towards the medial axis of uterus during contraction was discovered for 72.15% of the total cases, demonstrating that our RIIFT method is a potential tool to investigate EHG propagation for advancing our understanding human uterine excitability. CONCLUSIONS We developed a new approach and applied it to multichannel human EHG recordings to investigate the electrical signal propagation involved in uterine contraction. This provides an important platform for future studies to fill knowledge gaps in the spatiotemporal patterns of electrical excitation of the human uterus.
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Affiliation(s)
- Yuhang Xu
- Research Center for Intelligent Healthcare, Institute of Health and Wellbeing, Coventry University, Priory Street, Coventry, CV1 5FB, UK.
| | - Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Michael J Taggart
- Biosciences Institute, Newcastle University, International Center for Life, Newcastle upon Tyne, NE1 4EP, UK
| | - Dingchang Zheng
- Research Center for Intelligent Healthcare, Institute of Health and Wellbeing, Coventry University, Priory Street, Coventry, CV1 5FB, UK.
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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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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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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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Schwartz N, Mhajna M, Moody HL, Zahar Y, Shkolnik K, Reches A, Lowery CL. Novel uterine contraction monitoring to enable remote, self-administered nonstress testing. Am J Obstet Gynecol 2022; 226:554.e1-554.e12. [PMID: 34762863 DOI: 10.1016/j.ajog.2021.11.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND The serial fetal monitoring recommended for women with high-risk pregnancies places a substantial burden on the patient, often disproportionately affecting underprivileged and rural populations. A telehealth solution that can empower pregnant women to obtain recommended fetal surveillance from the comfort of their own home has the potential to promote health equity and improve outcomes. We have previously validated a novel, wireless pregnancy monitor that can remotely capture fetal and maternal heart rates. However, such a device must also detect uterine contractions if it is to be used to robustly conduct remote nonstress tests. OBJECTIVE This study aimed to describe and validate a novel algorithm that uses biopotential and acoustic signals to noninvasively detect uterine contractions via a wireless pregnancy monitor. STUDY DESIGN A prospective, open-label, 2-center study evaluated simultaneous detection of uterine contractions by the wireless pregnancy monitor and an intrauterine pressure catheter in women carrying singleton pregnancies at ≥32 0/7 weeks' gestation who were in the first stage of labor (ClinicalTrials.gov Identifier: NCT03889405). The study consisted of a training phase and a validation phase. Simultaneous recordings from each device were passively acquired for 30 to 60 minutes. In a subset of the monitoring sessions in the validation phase, tocodynamometry was also deployed. Three maternal-fetal medicine specialists, blinded to the data source, identified and marked contractions in all modalities. The positive agreement and false-positive rates of both the wireless monitor and tocodynamometry were calculated and compared with that of the intrauterine pressure catheter. RESULTS A total of 118 participants were included, 40 in the training phase and 78 in the validation phase (of which 39 of 78 participants were monitored simultaneously by all 3 devices) at a mean gestational age of 38.6 weeks. In the training phase, the positive agreement for the wireless monitor was 88.4% (1440 of 1692 contractions), with a false-positive rate of 15.3% (260/1700). In the validation phase, using the refined and finalized algorithm, the positive agreement for the wireless pregnancy monitor was 84.8% (2722/3210), with a false-positive rate of 24.8% (897/3619). For the subgroup who were monitored only with the wireless monitor and intrauterine pressure catheter, the positive agreement was 89.0% (1191/1338), with a similar false-positive rate of 25.4% (406/1597). For the subgroup monitored by all 3 devices, the positive agreement for the wireless monitor was significantly better than for tocodynamometry (P<.0001), whereas the false-positive rate was significantly higher (P<.0001). Unlike tocodynamometry, whose positive agreement was significantly reduced in the group with obesity compared with the group with normal weight (P=.024), the positive agreement of the wireless monitor did not vary across the body mass index groups. CONCLUSION This novel method to noninvasively monitor uterine activity, via a wireless pregnancy monitoring device designed for self-administration at home, was more accurate than the commonly used tocodynamometry and unaffected by body mass index. Together with the previously reported remote fetal heart rate monitoring capabilities, this added ability to detect uterine contractions has created a complete telehealth solution for remote administration of nonstress tests.
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Marinescu PS, Young RC, Miller LA, Llop JR, Pressman EK, Seligman NS. Mid-trimester uterine electromyography in patients with a short cervix. Am J Obstet Gynecol 2022; 227:83.e1-83.e17. [PMID: 35351409 DOI: 10.1016/j.ajog.2022.03.046] [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: 12/08/2021] [Revised: 03/08/2022] [Accepted: 03/22/2022] [Indexed: 11/01/2022]
Abstract
BACKGROUND Preterm birth is the largest single cause of infant death in the United States. A cervical length of <2.5 cm, measured in the mid-trimester, has been shown to identify individuals at increased risk. Uterine electromyography is an emerging technology for noninvasively assessing uterine bioelectrical activity. With its ability to characterize nuanced differences in myometrial signals, uterine electromyography assessments during the mid-trimester may provide insight into the mechanisms of cervical shortening. OBJECTIVE This study aimed to characterize uterine bioelectrical activity in pregnant individuals with short cervices in the mid-trimester compared with that of pregnant individuals of the same gestational age with normal cervical lengths. STUDY DESIGN This is a prospective cohort study of subjects with singleton, nonanomalous pregnancies between 16 weeks and 0 days and 22 weeks and 6 days of gestational age. Subjects with normal cervical length (≥3.0 cm) were compared with subjects with short cervical length (<2.5 cm). The short-cervical-length cohort was further stratified by history of preterm birth. Multichannel uterine electromyography recordings were obtained for ∼60 minutes using proprietary, directional electromyography sensors on the abdomen. Uterine electromyography signals were observed and classified in groups as spikes, short bursts, and bursts. Primary outcomes were relative expression of spike, short-burst, and burst uterine electromyography signals. Subgroup analyses assessed each signal percentage by cervical length, history of preterm birth, and gestational age at delivery. Differences in percentage of uterine electromyography signals according to cervical length were analyzed using nonparametric tests of significance. RESULTS Of the 28 included subjects, 10 had normal and 18 had short cervical length. There were 9 subjects with short cervical length and a history of preterm birth. Spikes were the most commonly recorded signals and were higher in the normal-cervical-length cohort (96.3% [interquartile range, 93.1%-100.0%]) than the short-cervical-length cohort (75.2% [interquartile range, 66.7%-92.0%], P=.001). In contrast, median percentages of short-bursts and bursts were significantly higher in subjects with a short cervical length (17.3% [interquartile range, 13.6%-23.9%] vs 2.5% for normal cervical length [interquartile range, 0%-5.5%], P=.001 and 6.6% [interquartile range, 0%-13.4%] vs 0% for normal cervical length [interquartile range, 0%-2.8%], P=.014, respectively). Within subgroup analyses, cervical length was inversely proportional to percentage of observed short-bursts (P=.013) and bursts (P=.014). Subjects with short cervical length and history of preterm birth had higher burst percentages (12.8% [interquartile range, 9.0%-15.7%]) than those with short cervical length and no history of preterm birth (3.3% [interquartile range, 0%-5.0%], P=.003). CONCLUSION Short-burst and burst uterine electromyography signals are observed more frequently in mid-trimester patients with short cervical lengths. This relationship provides insight into abnormal myometrial activation in the mid-trimester and offers a plausible biophysiological link to cervical shortening.
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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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21
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El Dine K, Nader N, Khalil M, Marque C. Uterine Synchronization Analysis During Pregnancy and Labor Using Graph Theory, Classification Based on Neural Network and Deep Learning. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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22
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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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23
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Jossou TR, Et-tahir A, Tahori Z, El Ouadi A, Medenou D, Bybi A, Fagbemi L, Sbihi M, Piaggio D. Electrodes in external electrohysterography: a systematic literature review. Biophys Rev 2021; 13:405-415. [PMID: 34178173 PMCID: PMC8214640 DOI: 10.1007/s12551-021-00805-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/04/2021] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND In low-income countries, pregnant women do not have easy access to health care, especially in rural and peri-urban areas. In this context, they can be surprised by the uterine contractions that precede childbirth and sometimes find themselves giving birth at home or on the way to the nearest health facility (located miles away from their home). In view of the development of an external uterine electrohysterogram acquisition system for labour prediction, a review of the literature on electrodes and their characteristics is necessary. METHODS A comprehensive literature review was conducted to collate information on the use of electrodes in external EHG recording and their characteristics. RESULTS Wet electrodes based on Ag/AgCl redox chemistry are the most common type of electrodes for EHG, employed in different configurations on the pregnant woman's abdomen. All positioning configurations are around the vertical median axis if they are not placed directly on it. Positioning below the navel seems to be the most efficient. The number of source, reference, and ground electrodes used varies from one author to another, as does the distance between the electrodes. CONCLUSION Two well-positioned source electrodes on the vertical median axis, with ground electrode on the right side of the hip and reference one on the left side, are able to generate a good external EHG recording signal. The minimum allowed inter-electrode distance is approximately 17.5 to 25mm.
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Affiliation(s)
- Thierry R. Jossou
- Department of Biomedical Engineering, Ecole Polytechnique d’Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
- Materials, Energy, Acoustics Team, Ecole Supérieure de Technologie de Salé, University Mohammed V, Rabat, Morocco
| | - Aziz Et-tahir
- Materials, Energy, Acoustics Team, Ecole Supérieure de Technologie de Salé, University Mohammed V, Rabat, Morocco
| | | | | | - Daton Medenou
- Department of Biomedical Engineering, Ecole Polytechnique d’Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
| | - Abdelmajid Bybi
- Materials, Energy, Acoustics Team, Ecole Supérieure de Technologie de Salé, University Mohammed V, Rabat, Morocco
| | - Latif Fagbemi
- Department of Biomedical Engineering, Ecole Polytechnique d’Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
| | - Mohamed Sbihi
- Materials, Energy, Acoustics Team, Ecole Supérieure de Technologie de Salé, University Mohammed V, Rabat, Morocco
| | - Davide Piaggio
- School of Engineering, University of Warwick, Coventry, CV4 7AL UK
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Methods to distinguish labour and pregnancy contractions: a systematic literature review. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00563-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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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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26
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Batista AG, Cebola R, Esgalhado F, Russo S, dos Reis CRP, Serrano F, Vassilenko V, Ortigueira M. The contractiongram: A method for the visualization of uterine contraction evolution using the electrohysterogram. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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27
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Moni SS, Kirshenbaum R, Comfort L, Kuba K, Wolfe D, Xie X, Negassa A, Bernstein PS. Noninvasive monitoring of uterine electrical activity among patients with obesity: a new external monitoring device. Am J Obstet Gynecol MFM 2021; 3:100375. [PMID: 33852969 DOI: 10.1016/j.ajogmf.2021.100375] [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: 02/14/2021] [Revised: 03/29/2021] [Accepted: 04/06/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Tocodynamometry is a common, noninvasive tool used to measure contraction frequency; however, its utility is often limited in patients with obesity. An intrauterine pressure catheter provides a more accurate measurement of uterine contractions but requires ruptured membranes, limiting its utility during early latent labor. Electrical uterine myography has shown promise as a noninvasive contraction monitor with efficacy similar to that of the intrauterine pressure catheter; however, its efficacy has not been widely studied in the obese population. OBJECTIVE This study aimed to validate the accuracy of electrical uterine myography by comparing it with tocodynamometry and intrauterine pressure catheters among laboring patients with obesity. STUDY DESIGN This was a prospective observational study from February 2017 to April 2018 of patients with obesity, aged 18 years or older, who were admitted to the labor unit with viable singleton pregnancies and no contraindications for electromyography. Patients were monitored simultaneously with electrical myography and tocodynamometry or intrauterine catheter for more than 30 minutes. Two blinded obstetricians reviewed the tracings. The outcomes of interest were continuous and interpretable tracing, number of contractions, and timing and duration of contractions, interpreted as point estimates and associated 95% confidence intervals. RESULTS A total of 110 patients were enrolled (65 tocodynamometry, 55 intrauterine catheter). Electrical myography was significantly more interpretable during a 30-minute tracing (P=.001) and detected 39% more contractions than tocodynamometry (P<.0001; 95% confidence interval, 23%-57%), whereas there was no difference in the interpretability of tracings or number of contractions between electrical myography and an intrauterine catheter (P=.16; 95% confidence interval, -0.19 to 1.19). Patients who underwent simultaneous monitoring preferred the electrical myography device over tocodynamometry. CONCLUSION Electrical uterine myography is superior to tocodynamometry in the detection of intrapartum uterine contraction monitoring and comparable with internal contraction monitoring.
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Affiliation(s)
- Saila S Moni
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (Drs Moni, Kirshenbaum, Comfort, Kuba, Wolfe, and Bernstein); Department of Maternal-Fetal Medicine, OSF HealthCare, Peoria, IL (Dr Moni).
| | - Rachel Kirshenbaum
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (Drs Moni, Kirshenbaum, Comfort, Kuba, Wolfe, and Bernstein); Department of Obstetrics and Gynecology, Lenox Hill Hospital, New York, NY (Dr Kirshenbaum)
| | - Lizelle Comfort
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (Drs Moni, Kirshenbaum, Comfort, Kuba, Wolfe, and Bernstein)
| | - Kfier Kuba
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (Drs Moni, Kirshenbaum, Comfort, Kuba, Wolfe, and Bernstein)
| | - Diana Wolfe
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (Drs Moni, Kirshenbaum, Comfort, Kuba, Wolfe, and Bernstein)
| | - Xianhong Xie
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY (Drs Xie and Negassa)
| | - Abdissa Negassa
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY (Drs Xie and Negassa)
| | - Peter S Bernstein
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (Drs Moni, Kirshenbaum, Comfort, Kuba, Wolfe, and Bernstein)
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Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography. SENSORS 2021; 21:s21072496. [PMID: 33916679 PMCID: PMC8038321 DOI: 10.3390/s21072496] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/30/2022]
Abstract
Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.
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29
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Diab A, Boudaoud S, Karlsson B, Marque C. Performance comparison of coupling-evaluation methods in discriminating between pregnancy and labor EHG signals. Comput Biol Med 2021; 132:104308. [PMID: 33711558 DOI: 10.1016/j.compbiomed.2021.104308] [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: 08/10/2020] [Revised: 02/27/2021] [Accepted: 02/27/2021] [Indexed: 01/13/2023]
Abstract
BACKGROUND Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing - and hence the interpretation of measurement results - is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question. METHOD Our methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h2), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar). RESULTS Our results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h2. To this end we propose a new approach (detailed below) for h2 called Filtered-Windowed (FW) h2 that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps. CONCLUSIONS This is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.
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Affiliation(s)
- Ahmad Diab
- Lebanese University, Faculty of public health, Beirut, Lebanon; Universités de Sorbonne, Université de Technologie de Compiègne, CNRS-UMR 7338 BMBI, 60200, Compiègne, France; Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
| | - Sofiane Boudaoud
- Universités de Sorbonne, Université de Technologie de Compiègne, CNRS-UMR 7338 BMBI, 60200, Compiègne, France.
| | - Brynjar Karlsson
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
| | - Catherine Marque
- Universités de Sorbonne, Université de Technologie de Compiègne, CNRS-UMR 7338 BMBI, 60200, Compiègne, France.
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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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Xu Y, Hao D, Zheng D. Analysis of Electrohysterographic Signal Propagation Direction during Uterine Contraction: the Application of Directed Information. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:21-25. [PMID: 33017921 DOI: 10.1109/embc44109.2020.9175423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The potential of using the information of uterine contractions (UCs) derived from electrohysterogram (EHG) has been recognized in early detection of preterm delivery. A better understanding of the conduction property of EHG is clinically useful for developing advanced methods to achieve a reliable prediction of preterm delivery. In this paper, a method to analyze the destination of EHG propagation has been proposed via the estimation of directed information (DI) between each pair of neighboring channels with a novel propagation terminal zone (PTZ) identification algorithm. The proposed method was applied to experimental data from the Icelandic 16-electrode EHG database. The results demonstrated that for more than 81.8% participants, the PTZ was identified along the medial axis of uterus, among which more than half have their PTZ determined in the center between the uterine fundus and public symphysis, which indicated a great probability of propagation of EHG signals towards the center of uterus plane.Clinical relevance- This study makes a fundamental contribution for predicting preterm delivery, which can provide improvement in obstetric care towards pregnancy monitoring.
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Xu Y, Liu H, Hao D, Taggart M, Zheng D. Uterus Modeling from Cell to Organ Level: towards Better Understanding of Physiological Basis of Uterine Activity. IEEE Rev Biomed Eng 2020; 15:341-353. [PMID: 32915747 DOI: 10.1109/rbme.2020.3023535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The relatively limited understanding of the physiology of uterine activation prevents us from achieving optimal clinical outcomes for managing serious pregnancy disorders such as preterm birth or uterine dystocia. There is increasing awareness that multi-scale computational modeling of the uterus is a promising approach for providing a qualitative and quantitative description of uterine physiology. The overarching objective of such approach is to coalesce previously fragmentary information into a predictive and testable model of uterine activity that, in turn, informs the development of new diagnostic and therapeutic approaches to these pressing clinical problems. This article assesses current progress towards this goal. We summarize the electrophysiological basis of uterine activation as presently understood and review recent research approaches to uterine modeling at different scales from single cell to tissue, whole organ and organism with particular focus on transformative data in the last decade. We describe the positives and limitations of these approaches, thereby identifying key gaps in our knowledge on which to focus, in parallel, future computational and biological research efforts.
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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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Uterine contractions clustering based on electrohysterography. Comput Biol Med 2020; 123:103897. [PMID: 32768044 DOI: 10.1016/j.compbiomed.2020.103897] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/12/2020] [Accepted: 06/27/2020] [Indexed: 11/20/2022]
Abstract
The uterine electromyogram, also named Electrohysterogram (EHG), is a non-invasive technique that has been used for pregnancy and labour monitoring as well as for research work on uterine physiology. This technique is well established in this field. There is however a vast unexplored potential in the EHG that is currently the subject of interdisciplinary research work involving different scientific fields such as medicine, engineering, physics and mathematics. In this paper, an unsupervised clustering method is applied to a previously obtained set of frequency spectral representations of the respective EHG signal contractions that were previously automatically detected and delineated. An innovative approach using the complete spectrum projection is described, rather than a set of relevant points. The feasibility of the method is established despite the concerns of possible computational burden incurred by the processing of the whole spectrum. Given the unsupervised nature of this classification, a validation procedure was performed whereas the obtained clusters were labelled through the correlation with the common knowledge about the most relevant uterine contraction types, as described in the literature. As a result of this study, a spectral description of the Alvarez contractions was obtained where it was possible to breakdown these important events in two different types according to their spectrum. Spectral estimates of Braxton-Hicks contractions were also obtained and associated to one of the clusters. This led to a full spectral characterization of these uterine events.
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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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Deep neural network for semi-automatic classification of term and preterm uterine recordings. Artif Intell Med 2020; 105:101861. [PMID: 32505424 DOI: 10.1016/j.artmed.2020.101861] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 02/25/2020] [Accepted: 04/14/2020] [Indexed: 02/04/2023]
Abstract
Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based DNN) is developed. The deep neural network has three layers including a stacked sparse autoencoder (SSAE) network with two hidden layers and one final softmax layer. To this end, the bursts of all 26 recordings of the publicly available TPEHGT DS database corresponding to uterine contraction intervals and non-contraction intervals (dummy intervals) were manually segmented. 20 features were extracted by two feature extraction algorithms including sample entropy and wavelet entropy. Afterwards, the SSAE network is adopted to learn high-level features from raw features by unsupervised learning. The softmax layer is added at the top of the SSAE network for classification. In order to verify the effectiveness of the proposed method, this study used 10-fold cross-validation and four indicators to evaluate classification performance. Experimental research results display that the performance of deep neural network can achieve Sensitivity of 98.2%, Specificity of 97.74%, and Accuracy of 97.9% in the publicly TPEHGT DS database. The performance of deep neural network outperforms the comparison models including deep belief networks (DBN) and hierarchical extreme learning machine (H-ELM). Finally, experimental research results reveal that the proposed method could be valid applied to semi-automatic identification of term and preterm uterine recordings.
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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: 3] [Impact Index Per Article: 0.8] [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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Mas-Cabo J, Prats-Boluda G, Garcia-Casado J, Alberola-Rubio J, Monfort-Ortiz R, Martinez-Saez C, Perales A, Ye-Lin Y. Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. SENSORS 2020; 20:s20092681. [PMID: 32397177 PMCID: PMC7248811 DOI: 10.3390/s20092681] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 04/24/2020] [Accepted: 05/07/2020] [Indexed: 12/22/2022]
Abstract
Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 ± 4.3% and 76.2 ± 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.
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Affiliation(s)
- J Mas-Cabo
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - G Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - J Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | | | - R Monfort-Ortiz
- Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain
| | - C Martinez-Saez
- Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain
| | - A Perales
- Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain
| | - Y Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
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Rooijakkers MJ, Rabotti C, Oei SG, Mischi M. Critical analysis of electrohysterographic methods for continuous monitoring of intrauterine pressure. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:3019-3039. [PMID: 32987514 DOI: 10.3934/mbe.2020171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Monitoring the progression of uterine activity provides important prognostic information during pregnancy and delivery. Currently, uterine activity monitoring relies on direct or indirect mechanical measurements of intrauterine pressure (IUP), which are unsuitable for continuous long-term observation. The electrohysterogram (EHG) provides a non-invasive alternative to the existing methods and is suitable for long-term ambulatory use. Several published state-of-the-art methods for EHG-based IUP estimation are here discussed, analyzed, optimized, and compared. By means of parameter space exploration, key parameters of the methods are evaluated for their relevance and optimal values. We have optimized all methods towards higher IUP estimation accuracy and lower computational complexity. Their accuracy was compared with the gold standard accuracy of internally measured IUP. Their computational complexity was compared based on the required number of multiplications per second (MPS). Significant reductions in computational complexity have been obtained for all published algorithms, while improving IUP estimation accuracy. A correlation coefficient of 0.72 can be obtained using fewer than 120 MPS. We conclude that long-term ambulatory monitoring of uterine activity is possible using EHG-based methods. Furthermore, the choice of a base method for IUP estimation is less important than the correct selection of electrode positions, filter parameters, and postprocessing methods. The presented review of state-of-the-art methods and applied optimizations show that long-term ambulatory IUP monitoring is feasible using EHG measurements.
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Affiliation(s)
| | - C Rabotti
- Signal Processing Systems, University of Technology Eindhoven, Eindhoven 5612 AZ, Netherlands
| | - S G Oei
- Perinatology and Obstetrics department, Maxima Medical Center, Veldhoven 5504 DB, Netherlands
| | - M Mischi
- Signal Processing Systems, University of Technology Eindhoven, Eindhoven 5612 AZ, Netherlands
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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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Peng J, Hao D, Yang L, Du M, Song X, Jiang H, Zhang Y, Zheng D. Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest. Biocybern Biomed Eng 2020; 40:352-362. [PMID: 32308250 PMCID: PMC7153772 DOI: 10.1016/j.bbe.2019.12.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.
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Key Words
- ACC, accuracy
- ADASYN, adaptive synthetic sampling approach
- ANN, artificial neural network
- AR, auto-regressive model
- AUC, the area under the curve
- CorrDim, correlation dimension
- DT, decision tree
- EHG, electrohysterogram
- Electrohysterogram (EHG)
- Feature extraction
- Gestational week
- IUPC, intrauterine pressure catheter
- K-NN, K-nearest
- LDA, linear discriminant analysis
- LE, Lyapunov exponent
- MDF, median frequency
- MNF, mean frequency
- PE, preterm delivery before the 26th week of gestation
- PF, peak frequency
- PL, preterm delivery after the 26th week of gestation
- Preterm delivery
- QDA, quadratic discriminant analysis
- RF, random forest
- RMS, root mean square
- ROC, the receiver operating characteristic curve
- Random forest (RF).
- SD, standard deviation
- SE, energy values in signal
- SM, maximum values in signal
- SS, singular values in signal
- SV, variance values in signal
- SVM, support vector machine
- SampEn, sample entropy
- TE, term delivery before the 26th week of gestation
- TL, term delivery after the 26th week of gestation
- TOCO, tocodynamometer
- TPEHG, term-preterm electrohysterogram
- Tr, time reversibility
- τz, zero-crossing
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Affiliation(s)
- Jin Peng
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Lin Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Mengqing Du
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Xiaoxiao Song
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Hongqing Jiang
- Beijing Haidian Maternal and Children Health Hospital, Beijing, China
| | - Yunhan Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Dingchang Zheng
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, UK
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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: 10] [Impact Index Per Article: 2.0] [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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Ellaithy M, Rasheed S, Shafik A, Abees S. Use of an antiemetic to shorten the length of labor in nulliparous women, exploring a potential role of an old drug: A randomized controlled trial. Int J Gynaecol Obstet 2019; 148:72-78. [PMID: 31609464 DOI: 10.1002/ijgo.12998] [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: 02/15/2019] [Revised: 08/13/2019] [Accepted: 10/11/2019] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To assess whether metoclopramide is effective in shortening the duration of the first stage of labor in primiparous women. METHODS The present randomized, double-blind, placebo-controlled trial was conducted at King Faisal Hospital, Saudi Arabia (between July 30, 2013, and September 1, 2016), and sequentially recruited young nulliparous women admitted in spontaneous active labor with or without ruptured membranes. Eligible participants were randomly assigned to receive a slow intravenous injection of either metoclopramide or placebo and consistently managed according to the local institutional intrapartum protocol and received identical monitoring and supportive care. The primary outcome was the cervical dilatation rate. RESULTS Fifty-nine women were included in the metoclopramide group and 52 in the placebo group. The first stage of labor was significantly shorter in the metoclopramide group (203 minutes vs 230 minutes in the placebo group, P=0.019), with a faster cervical dilatation rate (2.4 ± 0.4 cm/h vs 1.9 ± 0.5 cm/h in the placebo group, P<0.001) and shorter interval from treatment administration until full cervical dilatation. There was a significantly higher probability of faster delivery among women who were treated with metoclopramide (log-rank test, χ2 =5.997, P=0.014). CONCLUSION Metoclopramide safely reduced the duration of the first stage of labor and was not associated with major maternal or neonatal adverse outcomes. CLINICALTRIALS.GOV: NCT01937234.
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Affiliation(s)
- Mohamed Ellaithy
- Obstetrics and Gynecology Department, King Faisal Military Hospital, Khamis Mushait, Saudi Arabia.,Obstetrics and Gynecology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Suriaya Rasheed
- Obstetrics and Gynecology Department, King Faisal Military Hospital, Khamis Mushait, Saudi Arabia
| | - Adel Shafik
- Obstetrics and Gynecology Department, King Faisal Military Hospital, Khamis Mushait, Saudi Arabia
| | - Sara Abees
- Pharmacy Department, King Faisal Military Maternity Hospital, Khamis Mushait, Saudi Arabia
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45
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Benalcazar-Parra C, Garcia-Casado J, Ye-Lin Y, Alberola-Rubio J, Lopez Á, Perales-Marin A, Prats-Boluda G. New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring. Physiol Meas 2019; 40:085003. [PMID: 31370050 DOI: 10.1088/1361-6579/ab37db] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Uterine activity monitoring is an essential part of managing the progress of pregnancy and labor. Although intrauterine pressure (IUP) is the only reliable method of estimating uterine mechanical activity, it is highly invasive. Since there is a direct relationship between the electrical and mechanical activity of uterine cells, surface electrohysterography (EHG) has become a noninvasive monitoring alternative. The Teager energy (TE) operator of the EHG signal has been used for IUP continuous pressure estimation, although its accuracy could be improved. We aimed to develop new optimized IUP estimation models for clinical application. APPROACH We first considered enhancing the optimal estimation of IUP clinical features (maximum pressure and tonus) rather than optimizing the signal only (continuous pressure). An adaptive algorithm was also developed to deal with inter-patient variability. For each optimizing signal feature (continuous pressure, maximum pressure and tonus), individual (single patient), global (full database) and adaptive models were built to estimate the recorded IUP signal. The results were evaluated by computing the root mean square errors (RMSe): continuous pressure error (CPe), maximum pressure error (MPe) and tonus error (TOe). MAIN RESULTS The continuous pressure global model yielded IUP estimates with Cpe = 14.61 mm Hg, MPe = 29.17 mm Hg and Toe = 7.8 mm Hg. The adaptive models significantly reduced errors to CPe = 11.88, MPe = 16.02 and Toe = 5.61 mm Hg. The EHG-based IUP estimates outperformed those from traditional tocographic recordings, which had significantly higher errors (CPe = 21.93, MPe = 26.97, and TOe = 13.96). SIGNIFICANCE Our results show that adaptive models yield better IUP estimates than the traditional approaches and provide the best balance of the different errors computed for a better assessment of the labor progress and maternal and fetal well-being.
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Affiliation(s)
- Carlos Benalcazar-Parra
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, 46022 Valencia, Spain
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Tylcz JB, Muszynski C, Dauchet J, Istrate D, Marque C. An Automatic Method for the Segmentation and Classification of Imminent Labor Contraction From Electrohysterograms. IEEE Trans Biomed Eng 2019; 67:1133-1141. [PMID: 31352329 DOI: 10.1109/tbme.2019.2930618] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Preterm birth is the first cause of perinatal morbidity and mortality. Despite continuous clinical routine improvements, the preterm rate remains steady. Moreover, the specificity of the early diagnosis stays poor as many hospitalized women for preterm delivery threat finally deliver at term. In this context, the use of electrohysterograms may increase the sensitivity and the specificity of early diagnosis of preterm labor. METHODS This paper proposes a clinical application of electrohysterogram processing for the classification of patients as prone to deliver within a week or later. The approach relies on non-linear correlation analysis for the contraction bursts extraction and uses computation of various features combined with the use of Gaussian mixture models for their classification. The method is tested on a new dataset of 68 records collected on women hospitalized for preterm delivery threat. RESULTS This paper presents promising results for the automatic segmentation of the contraction and a classification sensitivity, specificity, and accuracy of, respectively, 80.7%, 76.3%, and 76.2%. CONCLUSION These results are in accordance with the gold standards but have the advantage to be non-invasive and could be performed at home. SIGNIFICANCE Diagnosis of imminent labor is possible by electrohysterography recording and may help in avoiding over-medication and in providing better cares to at-risk pregnant women.
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Huber C, Shazly SA, Ruano R. Potential use of electrohysterography in obstetrics: a review article. J Matern Fetal Neonatal Med 2019; 34:1666-1672. [PMID: 31303075 DOI: 10.1080/14767058.2019.1639663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Monitoring the uterine contraction during pregnancy is necessary to monitor labor progress, fetal and maternal well-being, and uterine activity. The aim of this review was to evaluate the performance of electrohysterography and to analyze the nature of uterine contraction. A search was undertaken using PubMed, Embase, and ClinicalTrials.gov database from 1 January 1950 to 1 November 2018. Search terms include: "Uterine" or "Uterus" or "Labor" or "Labour" and "electrical activity" or "electrohysterogram" or "electrohysterograph". Reviewing the literature, electrohysterography showed a higher sensitivity for uterine contraction detection and was independent of body mass index, abdominal wall thickness, or maternal position enabling monitoring obese patients as well. Electrohysterography can enhance uterine monitoring throughout labor because of its noninvasiveness, adhesive properties, and reduced obesity sensitiveness. Electrohysterography should be available to safely improve intrapartum monitoring instead of the invasive intrauterine pressure catheter.
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Affiliation(s)
- Carola Huber
- Department of Obstetrics and Gynaecology, Division of Maternal-Fetal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sherif A Shazly
- Department of Obstetrics and Gynaecology, Division of Maternal-Fetal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rodrigo Ruano
- Department of Obstetrics and Gynaecology, Division of Maternal-Fetal Medicine, Mayo Clinic, Rochester, MN, USA
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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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Domino M, Domino K, Pawlinski B, Sady M, Gajewska M, Gajewski Z. Computational multivariate modelling of electrical activity of the porcine uterus during spontaneous and hormone-induced oestrus. Exp Physiol 2019; 104:322-333. [PMID: 30615243 DOI: 10.1113/ep087451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 01/03/2019] [Indexed: 11/08/2022]
Abstract
NEW FINDINGS What is the central question of this study? Does oestrous cycle synchronization influence myoelectrical activity of porcine myometrium? What is the main finding and its importance? Exogenous hormones used to synchronize oestrus in pigs altered myoelectrical activity, which was effectively modelled. Higher-order multivariate statistic modelling provided evidence of similar activity in both types of oestrus, but a larger order of EMG signals during induced oestrus. Higher-order statistical analysis of the probabilistic model suggests the beginning of the early follicular phase and the mid-luteal phase to be most important in evaluation of the natural patterns of myoelectrical activity. Higher-order multivariate cumulants are more informative than classical statistics in characterization of myoelectrical activity changes in porcine myometrium. ABSTRACT In pig production units, control of the oestrous cycle and synchronization of ovulation have become routine herd management procedures. During the oestrous cycle, in both induced and spontaneous conditions, the ovaries and the uterus undergo hormone-dominated physiological changes, which are consistent with the hypothesis that there is a functional role of uterine contractions in promoting fertilization. We have used electromyography to determine whether the use of exogenous hormones, such as equine chorionic gonadotrophin and human chorionic gonadotrophin, which have the potential to control the timing of ovulation in female pigs, changes the multivariate relationships between parameters of electrical bursts and modulates the patterns of myoelectrical activity. We used the mathematical approach of higher-order multivariate cumulants in complex modelling of the myometrial electrical activity. The experiment was conducted on 12 mature Polish Landrace sows, and uterine activity was recorded during both spontaneous and induced oestrous cycles. The burst parameters were determined using six features in the time domain and, after Fast Fourier transformation, in the frequency domain. Evaluation of myoelectrical activity patterns was conducted based on classical univariate statistical methods and multivariate probabilistic modelling. The classical statistical approach indicated weaker myoelectrical activity after hormonal stimulation, whereas the higher-order multivariate statistical model showed evidence of similar status of activity and a larger order of signals during induced oestrus. Routine oestrous cycle synchronization affects the multivariate probabilistic model of myometrial electrical activity.
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Affiliation(s)
- Malgorzata Domino
- Department of Large Animal Diseases with Clinic, Faculty of Veterinary Medicine, Warsaw University of Life Sciences - Szkola Glowna Gospodarstwa Wiejskiego, Nowoursynowska 100, 02-797, Warsaw, Poland.,Veterinary Research Centre and Center for Biomedical Research, Faculty of Veterinary Medicine, WULS - SGGW, Nowoursynowska 100, 02-797, Warsaw, Poland
| | - Krzysztof Domino
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland
| | - Bartosz Pawlinski
- Department of Large Animal Diseases with Clinic, Faculty of Veterinary Medicine, Warsaw University of Life Sciences - Szkola Glowna Gospodarstwa Wiejskiego, Nowoursynowska 100, 02-797, Warsaw, Poland.,Veterinary Research Centre and Center for Biomedical Research, Faculty of Veterinary Medicine, WULS - SGGW, Nowoursynowska 100, 02-797, Warsaw, Poland
| | - Maria Sady
- Department of Large Animal Diseases with Clinic, Faculty of Veterinary Medicine, Warsaw University of Life Sciences - Szkola Glowna Gospodarstwa Wiejskiego, Nowoursynowska 100, 02-797, Warsaw, Poland.,Veterinary Research Centre and Center for Biomedical Research, Faculty of Veterinary Medicine, WULS - SGGW, Nowoursynowska 100, 02-797, Warsaw, Poland
| | - Magdalena Gajewska
- Veterinary Research Centre and Center for Biomedical Research, Faculty of Veterinary Medicine, WULS - SGGW, Nowoursynowska 100, 02-797, Warsaw, Poland
| | - Zdzislaw Gajewski
- Department of Large Animal Diseases with Clinic, Faculty of Veterinary Medicine, Warsaw University of Life Sciences - Szkola Glowna Gospodarstwa Wiejskiego, Nowoursynowska 100, 02-797, Warsaw, Poland.,Veterinary Research Centre and Center for Biomedical Research, Faculty of Veterinary Medicine, WULS - SGGW, Nowoursynowska 100, 02-797, Warsaw, Poland
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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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