FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography

Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour

Alfirević, Ž., Devane, D., & Gyte, G. M. (2006) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006066 · Cited by 1,037

The emergence of fetal behaviour. I. Qualitative aspects

de Vries, J. I. P., Visser, G. H. A., & Prechtl, H. F. R. (1982) · Early Human Development · doi:10.1016/0378-3782(82)90033-0 · Cited by 896

Are there behavioural states in the human fetus?

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Uncertain Value of Electronic Fetal Monitoring in Predicting Cerebral Palsy

Nelson, K. B., Dambrosia, J. M., Ting, T. Y., & Grether, J. K. (1996) · New England Journal of Medicine · doi:10.1056/nejm199603073341001 · Cited by 530

Choice of instruments for assisted vaginal delivery

O’Mahony, F., Hofmeyr, G. J., & Menon, V. (2010) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd005455.pub2 · Cited by 352

Combined spinal-epidural versus epidural analgesia in labour

Simmons, S. W., Taghizadeh, N., Dennis, A. T., Hughes, D., & Cyna, A. M. (2012) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd003401.pub3 · Cited by 335

The emergence of fetal behaviour. II. Quantitative aspects

de Vries, J. I. P., Visser, G. H. A., & Prechtl, H. F. R. (1985) · Early Human Development · doi:10.1016/0378-3782(85)90174-4 · Cited by 329

Betamimetics for inhibiting preterm labour

Neilson, J. P., West, H., & Dowswell, T. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd004352.pub3 · Cited by 300

Intrapartum electronic fetal heart rate monitoring versus intermittent auscultation: A meta-analysis

Vintzileos, A. M. (1995) · Obstetrics and Gynecology · doi:10.1016/0029-7844(94)00320-d · Cited by 273

Cardiotocography versus intermittent auscultation of fetal heart on admission to labour ward for assessment of fetal wellbeing

Devane, D., Lalor, J., Daly, S., McGuire, W. L., Cuthbert, A., & Smith, V. A. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd005122.pub5 · Cited by 231

Dissecting the current caesarean section rate in Shanghai, China

Ming, Y., Li, M., Dai, F., Huang, R., Zhang, J., Zhang, L., Qin, M., Zhu, L., Yu, H., & Zhang, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-38606-7 · Cited by 30

Electronic intrapartum fetal monitoring: a systematic review of international clinical practice guidelines

Mohan, M., Ramawat, J., LaMonica, G. E., Jayaram, P., Fattah, S. A., Learmont, J. G., Bryan, C., Zaoui, S., Pullattayil, A. K., Konje, J. C., et al. (2021) · AJOG Global Reports · doi:10.1016/j.xagr.2021.100008 · Cited by 30

Supervised and Unsupervised Learning of Fetal Heart Rate Tracings with Deep Gaussian Processes

Feng, G., Quirk, J. G., & Djurić, P. M. (2018) · Symposium on Neural Networks and Applications (NEUREL) · doi:10.1109/neurel.2018.8586992 · Cited by 30

Computerized analysis of cardiotocograms and ST signals is associated with significant reductions in hypoxic-ischemic encephalopathy and cesarean delivery: an observational study in 38,466 deliveries

Lopes-Pereira, J., Costa, A., Ayres‐de‐Campos, D., Santos, C., Amaral, J. S., & Bernardes, J. (2018) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2018.12.037 · Cited by 30

Diagnostic accuracy of Doppler ultrasound in predicting perinatal outcome in pregnancies at term: A prospective longitudinal study

D’Antonio, F., Rizzo, G., Gustapane, S., Buca, D. I. P., Flacco, M. E., Martellucci, C. A., Manzoli, L., Makatsariya, A. D., Nappi, L., Pagani, G. A., et al. (2019) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13705 · Cited by 30

The future of electronic fetal monitoring

Knupp, R. J., Andrews, W. W., & Tita, A. T. N. (2020) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2020.02.004 · Cited by 29

Advancements in Fetal Heart Rate Monitoring: A Report on Opportunities and Strategic Initiatives for Better Intrapartum Care

Lovers, A. A. K., Däumer, M., Frasch, M. G., Ugwumadu, A. H. N., Warrick, P. A., Vullings, R., Pini, N., Tolladay, J., Petersen, O. B., Lederer, C., et al. (2025) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.18097 · Cited by 31

CTG Analyzer: A graphical user interface for cardiotocography

Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Nardo, F. D., Fioretti, S., & Burattini, L. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037391 · Cited by 29

Comparison between cerebroplacental ratio and umbilicocerebral ratio in predicting adverse perinatal outcome at term

Mascio, D. D., Rizzo, G., Buca, D. I. P., D’Amico, A., Leombroni, M., Tinari, S., Giancotti, A., Muzii, L., Nappi, L., Liberati, M., et al. (2020) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2020.07.032 · Cited by 29

A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

Valderrama, C. E., Ketabi, N., Marzbanrad, F., Rohloff, P. J., & Clifford, G. D. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abc4c7 · Cited by 29

Maternal and neonatal outcomes of pregnancies complicated by late fetal growth restriction undergoing induction of labor with dinoprostone compared with cervical balloon: A retrospective, international study

Mascio, D. D., Villalaín, C., Rizzo, G., Morales‐Roselló, J., Sileo, F. G., Maruotti, G. M., Prefumo, F., Galindo, A., & D’Antonio, F. (2021) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.14135 · Cited by 29

A deep feature fusion network for fetal state assessment

Xiao, Y., Lu, Y., Liu, M., Zeng, R., & Bai, J. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.969052 · Cited by 29

Improving the interpretation of electronic fetal monitoring: the fetal reserve index

Evans, M. I., Britt, D. W., Evans, S. M., & Devoe, L. D. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.11.1275 · Cited by 29

DeepCTG® 1.0: an interpretable model to detect fetal hypoxia from cardiotocography data during labor and delivery

M’Barek, I. B., Jauvion, G., Vitrou, J., Holmström, E., Koskas, M., & Ceccaldi, P. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1190441 · Cited by 28

Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI

Innab, N., Alsubai, S., Alabdulqader, E. A., Alarfaj, A. A., Umer, M., Treľová, S., & Ashraf, I. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1462693 · Cited by 29

Sparse learning for Intrapartum fetal heart rate analysis

Abry, P., Spilka, J., Leonarduzzi, R., Chudáček, V., Pustelnik, N., & Doret, M. (2018) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/aabc64 · Cited by 28

Ultrasound prediction of adverse perinatal outcome at diagnosis of late‐onset fetal growth restriction

Dall’Asta, A., Stampalija, T., Mecacci, F., Minopoli, M., Schera, G. B. L., Cagninelli, G., Ottaviani, C., Fantasia, I., Barbieri, M., Lisi, F., et al. (2021) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.23714 · Cited by 30

Fetal heart rate estimation by non-invasive single abdominal electrocardiography in real clinical conditions

Dia, N., Fontecave-Jallon, J., Rojas, M. R., Faisant, M., Equy, V., Riethmuller, D., Guméry, P.-Y., & Rivet, B. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103187 · Cited by 28

FHRGAN: Generative adversarial networks for synthetic fetal heart rate signal generation in low-resource settings

Zhang, Y., Zhao, Z., Deng, Y., & Zhang, X. (2022) · Information Sciences · doi:10.1016/j.ins.2022.01.070 · Cited by 28

Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study

Daydulo, Y. D., Thamineni, B. L., Dasari, H. K., & Aboye, G. T. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02068-1 · Cited by 28

Labor Induction in Late-Onset Fetal Growth Restriction: Foley Balloon versus Vaginal Dinoprostone

Villalaín, C., Herraı̀z, I., Quezada, M. S., Gómez‐Arriaga, P. I., Simon, E. D. S., Gómez‐Montes, E., & Galindo, A. (2018) · Fetal Diagnosis and Therapy · doi:10.1159/000491784 · Cited by 28

Fetal heart rate monitoring practices at a public hospital in Northern Uganda – what health workers document, do and say

Ayebare, E. O., Jonas, W., Ndeezi, G., Nankunda, J., Hanson, C., Tumwine, J. K., & Hjelmstedt, A. (2020) · Global Health Action · doi:10.1080/16549716.2020.1711618 · Cited by 27

Shared decision‐making and maternity care in the deep learning age: Acknowledging and overcoming inherited defeaters

Begley, K., Begley, C., & Smith, V. A. (2020) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13515 · Cited by 29

Intrapartal cardiotocographic patterns and hypoxia-related perinatal outcomes in pregnancies complicated by gestational diabetes mellitus

Tarvonen, M. J., Hovi, P., Sainio, S., Vuorela, P., Andersson, S., & Teramo, K. A. (2021) · Acta Diabetologica · doi:10.1007/s00592-021-01756-0 · Cited by 27

Physiological control of fetal heart rate variability during labour: implications and controversies

Tournier, A., Beacom, M. J., Westgate, J. A., Bennet, L., Garabédian, C., Ugwumadu, A. H. N., Gunn, A. J., & Lear, C. A. (2021) · The Journal of Physiology · doi:10.1113/jp282276 · Cited by 27

Integrated S-Transform-Based Learning System for Detection of Arrhythmic Fetus

Gupta, K., Bajaj, V., & Ansari, I. A. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3271739 · Cited by 28

A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

Cömert, Z., Şengür, A., Akbulut, Y., Budak, Ü., Kocamaz, A. F., & Güngör, S. (2019) · IRBM · doi:10.1016/j.irbm.2019.05.003 · Cited by 26

Fetal defenses against intrapartum head compression—implications for intrapartum decelerations and hypoxic-ischemic injury

Lear, C. A., Westgate, J. A., Bennet, L., Ugwumadu, A. H. N., STONE, P. R., Tournier, A., & Gunn, A. J. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2021.11.1352 · Cited by 26

Computerized Analysis of Antepartum Cardiotocography: A Review

Jones, G. D., Cooke, W. R., Vatish, M., & Redman, C. (2022) · Maternal-Fetal Medicine · doi:10.1097/fm9.0000000000000141 · Cited by 26

Risk factors for hypoxic–ischemic encephalopathy in cases of severe acidosis: A case–control study

Lorain, P., Bower, A., Gottardi, E., Dommergues, M., Foix-L'Hélias, L., Guellec, I., & Kayem, G. (2022) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.14326 · Cited by 26

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings

Boudet, S., de l’Aulnoit, A. H., Peyrodie, L., Demailly, R., & de l’Aulnoit, D. H. (2022) · Biosensors · doi:10.3390/bios12090691 · Cited by 27

Intrapartum cardiotocography with simultaneous maternal heart rate registration improves neonatal outcome

Tarvonen, M. J., Markkanen, J., Tuppurainen, V., Jernman, R. M., Stefanović, V., & Andersson, S. (2024) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2024.01.011 · Cited by 26

Sensors for Fetal Hypoxia and Metabolic Acidosis: A Review

Cummins, G., Kremer, J., Bernassau, A. L., Brown, A., Bridle, H. L., Schulze, H., Bachmann, T. T., Crichton, M. L., Denison, F. C., & Desmulliez, M. P. Y. (2018) · Sensors · doi:10.3390/s18082648 · Cited by 26

Role of sFlt-1/PlGF ratio and feto-maternal Doppler for the prediction of adverse perinatal outcome in late-onset pre-eclampsia

Graupner, O., Karge, A., Flechsenhar, S., Seiler, A., Haller, B., Ortiz, J. U., Lobmaier, S. M., Axt-Fliedner, R., Enzensberger, C., Abel, K., et al. (2019) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-019-05365-9 · Cited by 26

Impaired validity of the new FIGO and Swedish CTG classification templates to identify fetal acidosis in the first stage of labor

Ekengård, F., Cardell, M., & Herbst, A. (2021) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2020.1869931 · Cited by 26

Technical characteristics of current cardiotocographic monitors

Ayres‐de‐Campos, D., & Reis, Z. S. N. (2015) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2015.05.005 · Cited by 26

Detection of Fetal Cardiac Anomaly from Composite Abdominal Electrocardiogram

Anisha, M., Kumar, S. S., Nithila, E. E., & Benisha, M. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102308 · Cited by 25

A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”

Cömert, Z., & Kocamaz, A. F. (2017) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2017.8090210 · Cited by 25

Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

Fuentealba, P., Manríquez, A. I., & Ortmeier, F. (2019) · IEEE Access · doi:10.1109/access.2019.2950798 · Cited by 25

Neurological damage arising from intrapartum hypoxia/acidosis

Rei, M., Ayres‐de‐Campos, D., & Bernardes, J. (2015) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2015.04.011 · Cited by 28

Fetal heart rate monitoring in labor: from pattern recognition to fetal physiology

OIKONOMOU, M., & Chandraharan, E. (2021) · Minerva Obstetrics and Gynecology · doi:10.23736/s2724-606x.20.04666-3 · Cited by 24

Recommendations for intrapartum fetal monitoring are not followed in low-risk women: A study from two Norwegian birth units

Rosset, I. K., Lindahl, K., Blix, E., & Kaasen, A. (2020) · Sexual & Reproductive Healthcare · doi:10.1016/j.srhc.2020.100552 · Cited by 24

Short‐term morbidity and types of intrapartum hypoxia in the newborn with metabolic acidaemia: a retrospective cohort study

di Pasquo, E., Commare, A., Masturzo, B., Paolucci, S., Cromi, A., Montersino, B., Germano, C., Attini, R., Perrone, S., Pisani, F., et al. (2022) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.17133 · Cited by 24

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation

Galli, A., Peri, E., Rabotti, C., Ouzounov, S. F., & Mischi, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3212587 · Cited by 24

eCTG: an automatic procedure to extract digital cardiotocographic signals from digital images

Sbrollini, A., Agostinelli, A., Marcantoni, I., Morettini, M., Burattini, L., Nardo, F. D., Fioretti, S., & Burattini, L. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.12.030 · Cited by 23

The effect of intrauterine resuscitation by maternal hyperoxygenation on perinatal and maternal outcome: a randomized controlled trial

Moors, S., Bullens, L. M., Heimel, P. J. V. R., Dieleman, J. P., Kulik, W., Bakkeren, D. L., van den Heuvel, E. R., der Jagt, M. B. V. D. H., & Oei, S. G. (2020) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2020.100102 · Cited by 23

Methods for Monitoring Risk of Hypoxic Damage in Fetal and Neonatal Brains: A Review

Uzianbaeva, L., Yan, Y., Joshi, T., Yin, N., Hsu, C., Hernandez‐Andrade, E. A., & Mehrmohammadi, M. (2021) · Fetal Diagnosis and Therapy · doi:10.1159/000520987 · Cited by 23

Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project

Aeberhard, J. L., Radan, A., Soltani, R. A., Strahm, K. M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., & Surbek, D. (2024) · Methods and Protocols · doi:10.3390/mps7010005 · Cited by 23

Recovery of missing samples in fetal heart rate recordings with Gaussian processes

Feng, G., Quirk, J. G., & Djurić, P. M. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081209 · Cited by 23

Safely lowering the emergency Cesarean and operative vaginal delivery rates using the Fetal Reserve Index

Eden, R. D., Evans, M. I., Britt, D. W., Evans, S. M., & Schifrin, B. S. (2018) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2018.1519799 · Cited by 23

Diagnostic Accuracy of Doppler Ultrasound in Predicting Perinatal Outcome in Appropriate for Gestational Age Fetuses: A Prospective Study

Buca, D. I. P., Rizzo, G., Gustapane, S., Mappa, I., Leombroni, M., Bascietto, F., Flacco, M. E., Martellucci, C. A., Manzoli, L., Nappi, L., et al. (2020) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/a-1072-5161 · Cited by 23

Introduction: Why is intrapartum foetal monitoring necessary – Impact on outcomes and interventions

Ayres‐de‐Campos, D. (2015) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2015.06.004 · Cited by 23

Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”

Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., & Burattini, L. (2020) · Data in Brief · doi:10.1016/j.dib.2020.105690 · Cited by 23

Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification

Mendis, L., Karmakar, D., Palaniswami, M. S., Brownfoot, F., & Keenan, E. (2025) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2025.3548401 · Cited by 22

Reliability in cardiotocography interpretation – impact of extended on‐site education in addition to web‐based learning: an observational study

Gyllencreutz, E., Varli, I. H., Lindqvist, P. G., & Holzmann, M. (2017) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13090 · Cited by 22

Hazard and cumulative incidence of umbilical cord metabolic acidemia at birth in fetuses experiencing the second stage of labor and pathologic intrapartum fetal heart rate requiring expedited delivery

Cavoretto, P. I., Seidenari, A., & Farina, A. (2022) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-022-06594-1 · Cited by 22

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Park, T. J., Chang, H. J., Choi, B., Jung, J. A., Kang, S., Yoon, S., Kim, M., & Yoon, D. (2022) · Yonsei Medical Journal · doi:10.3349/ymj.2022.63.7.692 · Cited by 22

Why is safety in intrapartum electronic fetal monitoring so hard? A qualitative study combining human factors/ergonomics and social science analysis

Lamé, G., Liberati, E. G., Canham, A., Burt, J., Hinton, L., Draycott, T. J., Winter, C., Dakin, F., Richards, N., Miller, L., et al. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2023-016144 · Cited by 22

A cross‐sectional comparison of three guidelines for intrapartum cardiotocography

Bhatia, M., Mahtani, K. R., Nunan, D., & Reddy, A. (2017) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.12161 · Cited by 22

Low sensitivity of the new FIGO classification system for electronic fetal monitoring to identify fetal acidosis in the second stage of labor

Ekengård, F., Cardell, M., & Herbst, A. (2020) · European Journal of Obstetrics & Gynecology and Reproductive Biology X · doi:10.1016/j.eurox.2020.100120 · Cited by 22

Automatic Evaluation of Fetal Heart Rate Based on Deep Learning

Liang, S., & Qia, L. (2021) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc51749.2021.9441583 · Cited by 22

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

Ribeiro, M., Monteiro-Santos, J., Castro, L., Antunes, L. F., Santos, C., Teixeira, A., & Henriques, T. S. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.661226 · Cited by 24

Unrecognized maternal heart rate artefact in cases of perinatal mortality reported to the United States Food and Drug Administration from 2009 to 2019: a critical patient safety issue

Kiely, D., Oppenheimer, L. W., & Dornan, J. C. (2019) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-019-2660-5 · Cited by 21

Ambulatory Monitoring of Maternal and Fetal using Deep Convolution Generative Adversarial Network for Smart Health Care IoT System

Venkatasubramanian, S. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130126 · Cited by 21

Perceptions and experiences of skilled birth attendants on using a newly developed strap-on electronic fetal heart rate monitor in Tanzania

Lafontan, S. R., Kidanto, H. L., Ersdal, H. L., Mbekenga, C. K., & Sundby, J. (2019) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-019-2286-7 · Cited by 21

Editorial: Fetal-maternal monitoring in the age of artificial intelligence and computer-aided decision support: A multidisciplinary perspective

Georgieva, A., Abry, P., Nunes, I., & Frasch, M. G. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2022.1007799 · Cited by 21

Wanting to be ‘with woman’, not with machine: Midwives’ experiences of caring for women being continuously monitored in labour

Fox, D. J., Coddington, R., & Scarf, V. L. (2021) · Women and Birth · doi:10.1016/j.wombi.2021.09.002 · Cited by 20

PA²Net: Period-Aware Attention Network for Robust Fetal ECG Detection

Wang, X., He, Z., Lin, Z., Han, Y., Liu, T., Lu, J., & Xie, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3189742 · Cited by 22

A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set

Das, S., Obaidullah, S. M., Mahmud, M., Kaiser, M. S., Roy, K., Saha, C. K., & Goswami, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-27707-z · Cited by 22

A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and Recursive Feature Elimination

Akmal, H., Hardalaç, F., & Ayturan, K. (2023) · Diagnostics · doi:10.3390/diagnostics13111931 · Cited by 20

Artificial intelligence based cardiotocogram assessment during labor

Melaet, R., de Vries, I. R., Kok, R. D., Oei, S. G., Huijben, I. A. M., van Sloun, R. J. G., van Laar, J. O. E. H., & Vullings, R. (2024) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2024.02.007 · Cited by 20

Radiological Modalities for the Assessment of Fetal Growth Restriction: A Comprehensive Review

Abdulqader, S. K., Nori, W., Akram, N. N., & Al-Kinani, M. (2024) · AL-Kindy College Medical Journal · doi:10.47723/nz221421 · Cited by 20

New FIGO and Swedish intrapartum cardiotocography classification systems incorporated in the fetal ECG ST analysis ( STAN ) interpretation algorithm: agreements and discrepancies in cardiotocography classification and evaluation of significant ST events

Olofsson, P., Norén, H., & Carlsson, A. (2017) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13277 · Cited by 20

Uterine Monitoring Techniques from Patients' and Users' Perspectives

Thijssen, K. M. J., Vlemminx, M. W. C., Westerhuis, M. E. M. H., Dieleman, J. P., der Jagt, M. B. V. D. H., & Oei, S. G. (2018) · American Journal of Perinatology Reports · doi:10.1055/s-0038-1669409 · Cited by 20

Fetal heart rate variability analysis for neonatal acidosis prediction

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Prediction of non-reassuring fetal status and umbilical artery acidosis by the maternal characteristic and ultrasound prior to induction of labor

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5.6 INTRAPARTUM FETAL HEART RATE MONITORING AIM

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