I have developed my career in biomedical research, building on my expertise in signal processing, computing and mathematics, but specialising in intrapartum (in labour) fetal monitoring. I am now leading an ambitious programme to develop evidence-based diagnostics in this clinical field. I am uniquely positioned to achieve this by working with the world’s largest and most complete birth cohort of routine labour data (>59,000 deliveries).
I obtained a BSc(Hons) in Applied Mathematics from the Technical University of Sofia (Bulgaria) and a PhD in Computer Science from Portsmouth University. I joined the Nuffield Department of Obstetrics and Gynaecology and the Institute of Biomedical Engineering at Oxford for a post-doctoral position in 2007. In 2012, I founded the Oxford Centre for Fetal Monitoring Technologies, of which I am the Scientific Director. In 2016 I was awarded a NIHR Career Development Fellowship to grow my independent research group.
BSc (Hons) PhD
Scientific Director, Oxford Centre for Fetal Monitoring Technologies
- NIHR Career Development Fellow
- Wolfson College Research Fellow
- Visiting Fellow, Department of Engineering Science
- Group Leader
I am leading a research team to develop a data-driven cardiotocography (CTG) system to continuously assess fetal wellbeing during term labour. CTG is the gold standard worldwide to detect if a fetus may benefit from an emergency delivery. Unreliable, empirical CTG interpretation will be replaced with quantified computer- and data-based individualised risk assessment.
We already have a prototype system (OxSys), as the starting point. It is derived from a large birth cohort (59,279 term deliveries) by systematic analysis of computer-based CTG features and clinical risk factors in relation to perinatal outcomes. In the coming years, we will continue to use such 'big data' to derive new understanding and improved methods for CTG interpretation in the patient-specific clinical context.
Beyond this, we will ensure refined optimal performance of OxSys on the birth cohort; validate OxSys on additional data (approx. 48,000 births); and develop a tablet for real-time wireless CTG analysis, moving OxSys from the 'lab' to the bedside.
Our work will potentially benefit families, clinicians and the NHS by reducing brain injuries, perinatal deaths and unnecessary interventions
National Institute for Health Research (NIHR)
Action Medical Research
I am keen to hear from interested students or post-doctoral researchers, especially as our team is currently growing.
I am very excited to be joining the new Oxford Big Data Institute which will provide further opportunities for growth.
For more information, including recent publications and up-coming international Workshop, please look at our group's website: Oxford Centre for Fetal Monitoring Technologies
You may also want to have a look at this page.
We are the host of the 2nd Workshop on Signal Processing and Monitoring (SPaM) in Labour: see webpage.
Phase-rectified signal averaging for intrapartum electronic fetal heart rate monitoring is related to acidaemia at birth.
Georgieva A. et al, (2014), BJOG, 121, 889 - 894
Umbilical cord gases in relation to the neonatal condition: The EveREst plot
Georgieva A. et al, (2013), European Journal of Obstetrics Gynecology and Reproductive Biology, 168, 155 - 160
Relation of fetal heart rate signals with unassignable baseline to poor neonatal state at birth.
Georgieva A. et al, (2012), Med Biol Eng Comput, 50, 717 - 725
Effect of signal acquisition method on the fetal heart rate analysis with phase rectified signal averaging
Georgieva A., Physiological Measurement
Understanding Fetal Heart Rate Patterns That May Predict Antenatal and Intrapartum Neural Injury
Lear CA. et al, (2018), Seminars in Pediatric Neurology
Doppler-based fetal heart rate analysis markers for the detection of early intrauterine growth restriction.
Stroux L. et al, (2017), Acta obstetricia et gynecologica Scandinavica, 96, 1322 - 1329
Computerized data-driven interpretation of the intrapartum cardiotocogram: a cohort study.
Georgieva A. et al, (2017), Acta obstetricia et gynecologica Scandinavica, 96, 883 - 891
Monitoring fetal maturation-objectives, techniques and indices of autonomic function.
Hoyer D. et al, (2017), Physiological measurement, 38, R61 - R88
Feature selection using genetic algorithms for fetal heart rate analysis.
Xu L. et al, (2014), Physiol Meas, 35, 1357 - 1371