Oxford Centre for Fetal Monitoring Technologies



Dr Antoniya Georgieva, Scientific Director, based at the Nuffield Department of Obstetrics and Gynaecology (NDOG) and The Big Data Institute, University of Oxford.

She is also an NIHR Career Development Fellow, a Wolfson College Research Fellow, and a Visiting Fellow at Department of Engineering Science.
Her background is in Applied Mathematics and Computer Science. Her interests lie in biomedical pattern recognition and data analysis applied to fetal monitoring.

Prof Chris Redman has been a member of the Nuffield Department of Obstetrics and Gynaecology since 1976. He pioneered the first commercial computerised antepartum fetal heart rate analysis (Sonicaid Dawes Redman) system. His interest is now to develop a comparable system for use in labour.

Prof Aris Papageorghiou (NDOG and St George's Hospital, London) has a joint appointment with St George’s Hospital in London and NDOG in Oxford. He brings clinical and research expertise to the project and facilitates collaborations between his two departments.

Prof Stephen Payne (Institute of Biomedical Engineering, IBME) has been part of the team since 2007. He facilitates collaborations between the IBME and this Centre, providing mentoring and joint supervision of students.

Mr Pawel Szafranski, Data Programmer, has been with us for several years now and has evolved to undertake various roles. For example, maintaining the digital CTG archive and extracting data from the new Electronic Patient Records. He was also responsible for updating and re-programming the Antepartum System.



Dr Ana Allen Alarcon and Dr Charles Roehr (Consultants, Neonatal Intensive Care Unit, Oxford University Hospitals NHS Foundation Trust).

Dr Austin Ugwumadu (Consultant, St George's University Hospitals NHS Foundation Trust, London).

Prof Annetine Staff (Professor at the University of Oslo and consultant at the Oslo University Hospitals).

Prof Ivan Jordanov (University of Portsmouth).



Hanna Lagenfelt and Sanna Skoglund, Erasmus medical students, Linkoeping University, Sweden
Alessio Petrozziello, PhD student, University of Portsmouth, UK



Ms Hannah van Scheepen (Technical Medicine Student, in collaboration with the University of Twente and Wilhelmina Kinderziekenhuis (UMCU), Utrecht)
Dr Lisa Stroux (DPhil student, with S. Payne and G. Clifford)
Dr Alex Clibbon (DPhil student, with S. Payne)
Dr Liang Xu (DPhil Student, with S. Payne)
Mr James Williams (4th Year Project, with G. Clifford)
Mr Charles Hardwick (4th Year Project, with S. Payne)
Ms Tingting Chen (Msc Project, with O. Burke)
Mr Henry Mariott (4th Year Project, with S. Payne)


Past collaborations

Dr Orlaith Burke (Nuffield Department of Population Health, University of Oxford): expertise in multivariate time series and time series cross-sectional models.

Dr Christina Aye (Clinical Researcher)

Ms Annie Laister and Ms Gillie Capp are Research Midwives from the University of Oxford OSPREA unit that recruit participants for our biomarkers studies and provide general advice.

Ms Miriam Willmott-Powell is a research midwife from OSPREA that helped with data collection for our Oxytocin augmentation in labour studies.

Ms Mary Moulden, Medical Technical Officer, is a core person behind the development of the Antepartum Monitoring Sytem. She has also been maintaining our large database of antepartum and intrapartum traces. She has now retired but remains a valuable member of the group.

Prof Gari Clifford was the head of the Intelligent Patient Monitoring Group at the Institute of Biomedical Engineering. His group has been working in fetal ECG analysis since 2005 and has numerous common interests. He has now moved on to another country but remains an important contact with the potential for future collaborations.

Ms Jacqueline Birks (Center for Statistics in Medicine) provided general statistical advice.

Dr Ben Fulcher and Dr Nick Jones (Physics Department) have developed a new tool for High Throughput Time-Series Analysis. In pilot studies, we applied it for the automated time-series extraction of electronic fetal monitoring features related to acidaemia at birth. The 10 most promising features have been selected for further study.