Article
Biology
Junhua Shen, Yan Liu, Meiyu Zhang, Alain Pumir, Liangshan Mu, Baohua Li, Jinshan Xu
Summary: This study demonstrates the effectiveness of analyzing and extracting features from EHG signals to quantify the synchrony and coherence of uterine contractions, showing superiority in predicting the onset of labor.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Biomedical
Alba Diaz-Martinez, Rogelio Monfort-Ortiz, Yiyao Ye-Lin, Javier Garcia-Casado, Mar Nieto-Tous, Felix Nieto-Del-Amor, Vicente Diago-Almela, Gema Prats-Boluda
Summary: The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has proven to be a better method than traditional clinical measures in monitoring labor progress. This study aimed to identify EHG biomarkers for predicting IOL success and evaluate the impact of myoelectrical response on parity. Significant and sustained differences were found between successful and failed groups, particularly in amplitude and non-linear parameters such as Spectral Entropy. Parous women showed a significantly higher amplitude progression rate compared to nulliparous women. These biomarkers can be used for early detection of induction failure risk and the development of more reliable and generalizable IOL success prediction systems.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Federica Sammali, Celine Blank, Tom G. H. Bakkes, Yizhou Huang, Chiara Rabotti, Benedictus C. Schoot, Massimo Mischi
Summary: In this study, a new method was proposed to predict the success of IVF by analyzing quantitative features extracted from EHG and TVUS recordings using machine learning. The results showed that KNN, SVM, and GMM machine learning models achieved high accuracy in different phases, with contraction frequency, unnormalized first moment, and standard deviation being the best feature selections.
Editorial Material
Engineering, Biomedical
Gilles Vandewiele, Femke Ongenae, Isabelle Dehaene
Summary: A recent study proposed a machine learning classifier to detect preterm birth early in pregnancy using a Fourier coefficient envelope. However, the evaluation of the system in the study was flawed, resulting in overly optimistic performance measures being reported.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Hui Wang, Zichao Wen, Wenjie Wu, Zhexian Sun, Zulfia Kisrieva-Ware, Yiqi Lin, Sicheng Wang, Hansong Gao, Haonan Xu, Peinan Zhao, Qing Wang, George A. Macones, Alan L. Schwartz, Phillip Cuculich, Alison G. Cahill, Yong Wang
Summary: Electromyometrial imaging (EMMI) has been developed to noninvasively and accurately image the 3D uterine electrical activation in sheep. In this study, the authors developed and applied a human EMMI system to image and evaluate 3D uterine electrical activation patterns during human term labor. The system successfully generated noninvasive uterine surface electrical potential maps, electrograms, and activation sequences using up to 192 electrodes, providing detailed 3D images and quantification of uterine contractions.
NATURE COMMUNICATIONS
(2023)
Article
Obstetrics & Gynecology
Maria W. E. Frenken, Kirsten M. J. Thijssen, Maria W. C. Vlemminx, Edwin R. van den Heuvel, Michelle E. M. H. Westerhuis, S. Guid Oei
Summary: This study evaluated the clinical outcomes of implementing electrohysterography during labor in high-risk deliveries, with no statistically significant differences observed in obstetric interventions and secondary outcomes between the electrohysterography and tocodynamometry groups. Further implementation and evaluation in clinical practice is recommended.
EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY
(2021)
Article
Engineering, Biomedical
S. Vinothini, N. Punitha, P. A. Karthick, S. Ramakrishnan
Summary: This study aims to develop an automated system for effective detection of preterm conditions using EHG signals and topological features. Results showed that the system could accurately differentiate term and preterm conditions with the maximum accuracy and positive predictive value of about 98.6%.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2021)
Review
Obstetrics & Gynecology
Mark I. Evans, David W. Britt, Shara M. Evans, Lawrence D. Devoe
Summary: Traditional electronic fetal monitoring (EFM) technology has failed to effectively prevent neonatal encephalopathy and cerebral palsy due to misunderstanding of screening and diagnostic tests, subjectivity and variability in interpretation, failure to address the pathophysiology of fetal compromise, and a narrow focus. New approaches such as the Fetal Reserve Index (FRI) are being developed to improve accuracy and early detection, while future artificial intelligence and machine learning advancements may further enhance clinical decision making during labor.
REPRODUCTIVE SCIENCES
(2022)
Review
Obstetrics & Gynecology
Mark I. Evans, David W. Britt, Shara M. Evans, Lawrence D. Devoe
Summary: Electronic fetal monitoring is the centerpiece of labor management, but its routine use has led to increased cesarean delivery rates. Our modified approach, called the Fetal Reserve Index, accurately identifies babies born with cerebral palsy and could reduce emergency cesarean deliveries. This index provides a good surrogate for fetal pH and base excess values.
AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY
(2023)
Article
Obstetrics & Gynecology
Vera Habraken, Merel J. M. Spanjers, Daisy A. A. Van der Woude, S. Guid Oei, Judith O. E. H. Van Laar
Summary: This study evaluated the use and preferences for fetal monitoring methods among Dutch obstetric care providers. The results showed that the most commonly used technique for fetal heart rate monitoring during labor in the Netherlands is the fetal scalp electrode, with the most common indication being inadequate external registration. Obstetric care providers would prefer a reliable non-invasive external registration method.
EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Hui Wang, Zichao Wen, Wenjie Wu, Zhexian Sun, Qing Wang, Alan L. Schwartz, Phillip Cuculich, Alison G. Cahill, George A. Macones, Yong Wang
Summary: This paper introduces a noninvasive electromyometrial imaging (EMMI) technique that can map uterine electrical activity onto the uterine surface during contractions and measure early activation regions and propagation patterns. This method allows for comprehensive monitoring of uterine contractions and aids in predicting the onset of labor.
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS
(2023)
Article
Engineering, Biomedical
Arnaldo G. Batista, Ricardo Cebola, Filipa Esgalhado, Sara Russo, Catarina R. Palma dos Reis, Fatima Serrano, Valentina Vassilenko, Manuel Ortigueira
Summary: Monitoring uterine contractions is important for assessing preterm risk, estimating term birth, and investigating uterine physiology. Multichannel EHG mapping provides valuable information, but interpreting the data poses challenges due to multiple contractions in different channels and recording sessions.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Endocrinology & Metabolism
Rocio Lizbeth Olmos-Ramirez, Miguel Angel Pena-Castillo, Hugo Mendieta-Zeron, Jose Javier Reyes-Lagos
Summary: This study aimed to compare the fetal autonomic response to uterine contractions between preterm and term labor. By analyzing the time-frequency features of fetal heart rate variability, it was found that the response of preterm and term fetuses to contractions differed.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Obstetrics & Gynecology
Antonina I. Frolova, Molly J. Stout, Ebony B. Carter, George A. Macones, Alison G. Cahill, Nandini Raghuraman
Summary: The study investigated the association between the use of internal monitors and maternal infectious morbidity among women with obesity, finding that internal monitor use was associated with an increased risk for infectious morbidity but with a weaker association in obese women. Additionally, the incidence of cesarean delivery was significantly higher among women who had internal monitors placed during labor.
AMERICAN JOURNAL OF OBSTETRICS & GYNECOLOGY MFM
(2021)
Article
Biotechnology & Applied Microbiology
Muhammad Mhajna, Boaz Sadeh, Simcha Yagel, Christof Sohn, Nadav Schwartz, Steven Warsof, Yael Zahar, Amit Reches
Summary: Remote, non-invasive detection and monitoring of uterine activity (UA) was achieved through the analysis of maternal electrocardiographic and phonocardiographic signals. The algorithm showed higher accuracy and reliability compared to the standard-of-care method TOCO. This method has the potential to greatly benefit women and providers seeking telehealth solutions for pregnancy care.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Anesthesiology
Michael J. Banner, Carl G. Tams, Neil R. Euliano, Paul J. Stephan, Trevor J. Leavitt, A. Daniel Martin, Nawar Al-Rawas, Andrea Gabrielli
JOURNAL OF CLINICAL MONITORING AND COMPUTING
(2016)
Article
Obstetrics & Gynecology
Tammy Y. Euliano, Minh Tam Nguyen, Shalom Darmanjian, John D. Busowski, Neil Euliano, Anthony R. Gregg
AMERICAN JOURNAL OF PERINATOLOGY
(2016)
Article
Critical Care Medicine
Carl G. Tams, Ali Ataya, Neil R. Euliano, Paul Stephan, A. Daniel Martin, Hassan Alnuaimat, Andrea Gabrielli
JOURNAL OF CRITICAL CARE
(2017)
Article
Anesthesiology
Tammy Y. Euliano, Kostas Michalopoulos, Savyasachi Singh, Anthony R. Gregg, Mariem Del Rio, Terrie Vasilopoulos, Amber M. Johnson, Allison Onkala, Shalom Darmanjian, Neil R. Euliano, Monique Ho
ANESTHESIA AND ANALGESIA
(2018)
Article
Anesthesiology
Michael J. Banner, Neil R. Euliano, David Grooms, A. Daniel Martin, Nawar Al-Rawas, Andrea Gabrielli
JOURNAL OF CLINICAL MONITORING AND COMPUTING
(2014)
Article
Obstetrics & Gynecology
Rodney K. Edwards, Neil R. Euliano, Savyasachi Singh, Rachel C. LeDuke, William W. Andrews, Victoria Jauk, Akila Subramaniam, Jeff M. Szychowski
AMERICAN JOURNAL OF PERINATOLOGY
(2019)
Article
Anesthesiology
Carl Tams, Paul J. Stephan, Neil R. Euliano, A. Daniel Martin, Rohit Patel, Ali Ataya, Andrea Gabrielli
JOURNAL OF CLINICAL MONITORING AND COMPUTING
(2020)
Article
Anesthesiology
Carl Tams, Paul Stephan, Neil Euliano, Andrea Gabrielli, A. Daniel Martin, Philip Efron, Rohit Patel
JOURNAL OF CLINICAL MONITORING AND COMPUTING
(2020)
Article
Critical Care Medicine
Carl G. Tams, Neil R. Euliano, A. Daniel Martin, Michael J. Banner, Andrea Gabrielli, Steven Bonnet, Paul J. Stephan, Adam J. Seiver, Michael A. Gentile
JOURNAL OF CRITICAL CARE
(2020)
Article
Engineering, Biomedical
Neil R. Euliano, Paul Stephan, Konstantinos Michalopoulos, Michael A. Gentile, Joseph Layon, Andrea Gabrielli
Summary: This study aims to explore an electronic platform adapted to a hand-held tablet that displays real-time ventilatory parameters to increase clinician awareness of key LPV parameters. Additionally, a handoff checklist is created to improve shift-change communication. Results from a simulated environment show that using this system significantly reduces the time above guideline Pplat and the time outside the VT range, while improving the quality of clinician handoffs.
MEDICAL DEVICES-EVIDENCE AND RESEARCH
(2022)
Article
Obstetrics & Gynecology
Tammy Euliano, Shalom Darmanjian, Minh Tam Nguyen, John Busowski, Neil Euliano, Anthony Gregg
JOURNAL OF PREGNANCY
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Gabriel Nallathambi, Jose C. Principe, Neil R. Euliano
2015 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING
(2015)
Proceedings Paper
Engineering, Biomedical
Carl G. Tams, Neil R. Euliano
2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2015)
Article
Critical Care Medicine
Nawar Al-Rawas, Michael J. Banner, Neil R. Euliano, Carl G. Tams, Jeff Brown, A. Daniel Martin, Andrea Gabrielli