Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

Fetal autonomic cardiac response during pregnancy and labour

Timely recognition of fetal distress, during pregnancy and labour, in order to intervene adequately is of major importance to avoid neonatal morbidity and mortality. As discussed in chapter 1, the cardiotocogram (CTG) might be a useful screening test for fetal monitoring but it has insufficient specificity and requires additional diagnostic tests in case of suspected fetal compromise to avoid unnecessary operative deliveries. Potential additional techniques used in clinical practice are fetal scalp blood sampling (FBS) and ST-waveform analysis of the fetal electrocardiogram (ECG; STAN®). However, publications on these techniques provide limited support for the use of these methods in the presence of a non-reassuring CTG for reducing caesarean sections. In addition, these techniques are invasive and can therefore only be used during labour at the term or the near term period. Consequently, it is of great clinical importance that additional methods are developed that contribute to more reliable assessment of fetal condition. Preferably, this information is obtained non-invasively. Valuable additional information on the fetal condition can possibly be obtained by spectral analysis of fetal heart rate variability (HRV). The fetal heart rate fluctuates under the control of the autonomic part of the central nervous system. The autonomic cardiac modulation is discussed in chapter 2. The sympathetic and parasympathetic nervous systems typically operate on partly different timescales. Time-frequency analysis (spectral analysis) of fetal beat-to-beat HRV can hence quantify sympathetic and parasympathetic modulation and characterise autonomic cardiac control . The low frequency (LF) component of HRV is associated with both sympathetic and parasympathetic modulation while the high frequency (HF) component is associated with parasympathetic modulation alone2. Spectral estimates of HRV might indirectly reflect fetal wellbeing and increase insight in the human fetal autonomic cardiac response. In chapter 3, technical details for retrieving fetal beat-to-beat heart rate and its spectral estimates are provided. In this thesis spectral analysis of fetal HRV is investigated. The first objective is to study the value of spectral analysis of fetal HRV as a tool to assess fetal wellbeing during labour at term. The second objective is to monitor spectral estimates of fetal HRV, non-invasively, during gestation to increase insight in the development of human fetal autonomic cardiac control. Since Akselrod reported the relation between autonomic nervous system modulation and LF and HF peaks in frequency domain1, frequency analysis of RR interval fluctuations is widely performed . For human adults, standards for HRV measurement and physiological interpretation have been developed2. Although HRV parameters are reported to be highly prognostic in human adults in case of cardiac disease, little research is done towards the value of these parameters in assessing fetal distress in the human fetus, as shown in chapter 4. In this chapter, the literature about time-frequency analysis of human fetal HRV is reviewed in order to determine the value of spectral estimates for fetal surveillance. Articles that described spectral analysis of human fetal HRV and compared the energy in spectral bands with fetal bloodgas values were included. Only six studies met our inclusion criteria. One study found an initial increase in LF power during the first stage of fetal compromise, which was thought to point to stress-induced sympathetic hyperactivity3. Five out of six studies showed a decrease in LF power in case of fetal distress , , , , ,. This decrease in LF power in case of severe fetal compromise was thought to be the result of immaturity or decompensation of the fetal autonomic nervous system. These findings support the hypothesis that spectral analysis of fetal HRV might be a promising method for fetal surveillance. All studies included in the literature review used absolute values of LF and HF power. Although absolute LF and HF power of HRV provide useful information on autonomic modulation, especially when considering fetal autonomic development, LF and HF power may also be measured in normalised units. Normalised LF (LFn) and normalised HF power (HFn) of HRV represent the relative value of each power component in proportion to the total power2. Adrenergic stimulation can cause a sympathetically-modulated increase in fetal heart rate . A negative correlation however exists between heart rate and HRV . As a result, the sympathetic stimulation can decrease the total power of HRV and even the absolute LF power. When normalising the absolute LF (and HF) with respect to the total power, a shift in activity from HFn to LFn might become visible, revealing the expected underlying sympathetic activity. Thus, because changes in total power influence absolute spectral estimates in the same direction, normalised values of LF and HF power seem more suitable for fetal monitoring. In other words, normalised spectral estimates detect relative changes that are no longer masked by changes in total power2. LFn and HFn power are calculated by dividing LF and HF power, respectively, by total power and represent the controlled and balanced behaviour of the two branches of the autonomic nervous system2. In chapter 5 we hypothesised that the autonomic cardiovascular control is functional in fetuses at term, and that LFn power increases in case of distress due to increased sympathetic modulation. During labour at term, ten acidaemic fetuses were compared with ten healthy fetuses. During the last 30 minutes of labour, acidaemic fetuses had significantly higher LFn power and lower HFn power than control fetuses, which points to increased sympathetic modulation. No differences in absolute LF or HF power were found between both groups. The observed differences in normalised spectral estimates of HRV were not observed earlier in labour. In conclusion, it seems that the autonomic nervous system of human fetuses at term responds adequately to severe stress during labour. Normalised spectral estimates of HRV might be able to discriminate between normal and abnormal fetal condition. Although we found significant differences in normalised spectral estimates between healthy and acidaemic fetuses, we wondered whether spectral power of HRV is also related to fetal distress in an earlier stage. The next step in chapter 6 was therefore, to investigate whether spectral estimates are related to fetal scalp blood pH during labour. Term fetuses during labour, in cephalic presentation, that underwent one or more scalp blood samples were studied. Beat-to-beat fetal heart rate segments, preceding the scalp blood measurement, were used to calculate spectral estimates. In total 39 FBS from 30 patients were studied. We found that normalised spectral estimates are related to fetal scalp blood pH while absolute spectral estimates are not related to fetal pH. It was further demonstrated that LFn power is negatively related and HFn power is positively related to fetal pH. These findings point to increased sympathetic and decreased parasympathetic cardiac modulation in human fetuses at term upon decrease of their pH value. This study confirms the hypothesis that normalised spectral values of fetal HRV are related to fetal distress in an early stage. Previous studies showed that absolute LF and HF power increase as pregnancy progresses, which is attributed to fetal autonomic maturation , . Since it is yet unclear how LFn and HFn evolve with progressing pregnancy, before using spectral analysis for fetal monitoring, it has to be determined whether gestational age has to be corrected for. In addition, fetal autonomic fluctuations, and thus spectral estimates of HRV, are influenced by fetal behavioural state . Since these states continue to change during labour , thorough understanding of the way in which these changes in state influence spectral power is necessary for the interpretation of spectral values during labour at term. Therefore, in chapter 7, we examined whether differences in spectral estimates exist between healthy near term and post term fetuses during labour. In case such differences do exist, they should be taken into consideration for fetal monitoring. The quiet and active sleep states were studied separately to determine the influence of fetal behavioural state on spectral estimates of HRV during labour around term. No significant differences in spectral estimates were found between near term and post term fetuses during active sleep. During quiet sleep, LFn power was lower and HF and HFn power were higher in post term compared to near term fetuses, no significant differences in LF power were observed between both groups. LF, HF and LFn power were higher and HFn power was lower during active sleep compared to quiet sleep in both groups. This seems to point to sympathetic predominance during the active state in fetuses around term. In addition, post term parasympathetic modulation during rest seems increased compared to near term. In conclusion, fetal behavioural state and gestational age cause a considerable variability in spectral estimates in fetuses during labour, around term, which should be taken into consideration when using spectral estimates for fetal monitoring. In chapters 4 to 6, spectral estimates of beat-to-beat fetal HRV were studied using fetal ECG recordings that were obtained directly from the fetal scalp during labour. However, the second objective of this thesis is to obtain spectral estimates non-invasively during gestation to increase insight in the development of human fetal autonomic cardiac control. The fetal ECG is also present on the maternal abdomen, although much smaller in amplitude and obscured by the maternal ECG and noise. Chapter 8 focused on non-invasive measurement of the fetal ECG from the maternal abdomen. These measurements allow for obtaining beat-to-beat fetal heart rate non-invasively. Therefore, this method can be used to obtain spectral estimates of fetal HRV throughout gestation. Although abdominal recording of the fetal ECG may offer valuable additional information, it is troubled by poor signal-to-noise ratios (SNR) during certain parts of pregnancy, e.g. during the immature period and during the vernix period. To increase the usability of abdominal fetal ECG recordings, an algorithm was developed that uses a priori knowledge on the physiology of the fetal heart to enhance the fetal ECG components in multi-lead abdominal fetal ECG recordings, before QRS-detection. Evaluation of the method on generated fetal ECG recordings with controlled SNR showed excellent results. The method for non-invasive fetal ECG and beat-to-beat heart rate detection presented in chapter 8 was used for analysis in chapter 9. The feasibility of this method in a longitudinal patient study was investigated. In addition, changes in spectral estimates of HRV during pregnancy were studied and related to fetal rest-activity state to study the development of fetal autonomic cardiac control. We found that approximately 3% of non-invasive fetal ECG recordings could be used for spectral analysis. Therefore, improvement of both equipment and algorithms is still needed to obtain more good-quality data. The percentage of successfully retrieved data depends on gestational age. Before 18 and between 30 and 34 weeks no good-quality beat-to-beat heart rate data were available. We found an increase in LF and HF power of fetal HRV with increasing gestational age, between 21 to 30 weeks of gestation. This increase in LF and HF power is probably due to increased sympathetic and parasympathetic modulation and might be a sign of autonomic development. Furthermore, we found sympathetic predominance during the active state compared to the quiet state in near term fetuses (34 to 41 weeks of gestation), comparable to the results observed during labour around term. During 34 to 41 weeks a (non-significant) decrease in LF and LFn power and a (non-significant) increase in HF and HFn power were observed. These non-significant changes in spectral estimates in near term fetuses might be associated with changes in fetal rest-activity state and increased parasympathetic modulation as pregnancy progresses. However, more research is needed to confirm this

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK).

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research

Intrapartum ultrasound in predicting labour outcome

Introduction: I sought to determine the acceptability and feasibility of intrapartum ultrasound (USS) and the predictive value of clinical and ultrasound parameters in the first stage of labour for intrapartum Caesarean delivery (ICD) based on single and repeat assessments. External validation of a single assessment labour prediction model and evaluation of a research based “Intrapartum app” was performed. Methods: A prospective observational study was carried out at Queen Charlotte’s and Chelsea Hospital between 24-42 weeks’ gestation to assess the acceptability and feasibility of intrapartum ultrasound. Pre-assessment and post-assessment modified validated acceptability questionnaires were evaluated. The predictive value of intrapartum ultrasound in the first stage of labour for Caesarean delivery was assessed in a prospective longitudinal observational cohort in nulliparous term (37-42 weeks) labouring women. Transabdominal ultrasound scan was performed to assess fetal Doppler, amniotic fluid, fetal head position and transperineal ultrasound for head-perineum distance (HPD), caput succedaneum and moulding of the fetal head. These assessments were repeated at the next digital vaginal examination (VE). Results: 119 women were recruited to assess the acceptability of intrapartum ultrasound, 104 completed both pre and post-assessment questionnaires. The negative experience score was higher for VE compared to USS pre (10 and 5, p<0.0001) and post assessment (8 and 4, p<0.0001). The feasibility of intrapartum USS was assessed with paired vaginal and USS assessments performed in 192 women. If a cervix measured ≤6cm on VE, there was a smaller difference between USS and VE measured cervical dilatation (bias -0.173, 95% limits of agreement -2.51 to 2.16cm reduced to -1.96 to 1.89cm). There was low agreement (bias -0.232, 95% limits of agreement -5.31 to 4.84 clock hours) on fetal head position and fair agreement for the presence of caput succedaneum (76%, p<0.05). Fetal head station and head perineum distance were negatively correlated (r=-0.57, p<0.0001). In the first stage prediction study, 270 patients were recruited, 269 patients were evaluable of whom 219 (81%) had repeat assessments. Intrapartum Caesarean delivery was required in 79 (29%) patients. On external validation, the new population performed less well (AUC 0.80 (95% CI 0.74 to 0.86) compared to that on which the 2015 single assessment model (AUC 0.85 (95% CI 0.678-1.000) was based. The length of labour was shorter for those patients predicted to be at “high” likelihood of vaginal delivery compared to those with a “medium” and “low” likelihood (log rank test, p<0.01) using the “Intrapartum app”. The main predictors at the first assessment were HPD (adjusted OR 1.08/mm, 95% CI 1.04-1.13, p<0.0001) and cervical dilatation (adjusted OR 0.78/cm, 0.66-0.92, p=0.0025). When considering repeat assessments, cervical dilatation change was the most important predictive variable (adjusted OR 0.10, 0.03-0.26, p<0.0001). After backward variable selection, a multivariable analysis of first scan information (n=264) included gestational age, HPD, cervical dilatation and caput succedaneum, this had an internally validated AUC of 0.72. Analysis of repeat scan information (n=214) included change in cervical dilatation and caput succedaneum, this had an internally validated AUC of 0.78. Conclusion: Intrapartum ultrasound in predicting labour outcome is acceptable to women and is feasible for certain labour parameters. Transabdominal ultrasound should be considered gold standard for defining fetal head position. Transperineal ultrasound’s role in measuring cervical dilatation is limited to early labour but it does allow assessment of caput succedaneum more readily. There is no direct relationship in assessing fetal head descent when directly comparing HPD to vaginal palpation of the fetal head station. Using the ‘Intrapartum app’, a single assessment model shows promise in predicting the length of labour and likelihood of vaginal delivery but performed differently when applied to a population with different baseline demographic features. A repeat ultrasound assessment model has added value for the intrapartum prediction of Caesarean delivery. Further work on developing a robust prediction model would be facilitated using the key risk factors identified in this work.Open Acces

Adjunctive technologies for intrapartum fetal monitoring: current perspectives and proof of concept for a novel approach

Fetal monitoring is a recurring theme in perinatal morbidity and mortality reports, highlighting the limitations of cardiotocography and current adjunctive technologies, such as fetal blood sampling (FBS). There is an unmet need for more robust methods of intrapartum fetal assessment. Microdialysis may help to detect babies at risk of hypoxia by monitoring trends in lactate and related metabolites from fetal scalp interstitial fluid in a minimally invasive manner. However, its clinical value remains unproven because there is limited evidence on the relationship between interstitial and arterial lactate. Translating advances in fetal monitoring technology into improved clinical outcomes also depends on how obstetricians use such technology in their practice, which few past studies have explored in depth. This research comprised two components. The first part aimed (1) to develop a neonatal piglet model of hyperlactataemia; and, using this model, (2) to investigate the relationship between interstitial and arterial lactate; and (3) to explore the feasibility of using subcutaneous microdialysis to monitor the metabolic response to hypoxia in vivo. Eight neonatal piglets were monitored under non-recovery general anaesthesia. Hyperlactataemia was achieved by means of alveolar hypoxia and/or intravenous sodium L-lactate infusion, with target lactate concentrations above 12 mmol/L. Microdialysate from two subcutaneous microdialysis catheters inserted into the scalp of each piglet was analysed for interstitial lactate, pyruvate, glucose and glutamate concentrations, which were compared to arterial blood gas measurements. A subset of dialysate samples underwent secondary analyses with the StatStrip Xpress® pointof- care lactate meter to assess its performance. In total, 432 dialysate samples were collected from seven piglets. There was variation in the piglets’ response to hypoxia therefore two piglets received lactate infusions, with four overall achieving target hyperlactataemia. Interstitial lactate, pyruvate and glucose concentrations were not affected by microdialysis catheter insertion. There was a strong positive correlation between arterial lactate and interstitial lactate, and weaker positive correlations with interstitial lactate-to-pyruvate and lactate-to-glucose ratios. Interstitial lactate mirrored trends in arterial lactate with an approximate time lag of 10 v to 20 min, although the closeness of agreement varied between piglets. StatStrip Xpress® lactate values showed a proportional negative bias relative to the reference microdialysis analyser, but trend data and assay precision were comparable. The second part of this research sought to understand how UK obstetricians use adjunctive fetal monitoring technologies and what factors influence their practice, as well as exploring attitudes towards new technology and other areas for improving practice. Data were collected through semi-structured telephone interviews with 16 obstetricians of varying career grade from nine maternity units across the UK, prior to thematic analysis. Most obstetricians reported performing FBS but attitudes towards it varied. The use of fetal monitoring technology was influenced by obstetricians’ individual clinical autonomy, the socio-cultural norms of their unit, and wider external factors, such as guidelines. Obstetricians recognised the limitations of current methods of monitoring, but enthusiasm towards new technology was checked by a scepticism of ‘computerisation’ and perceived barriers to changing practice; hence, better staff training was seen as the immediate priority for improving outcomes. In summary, the work presented in this thesis provides new insight into the current role of adjunctive technologies in UK obstetric practice and demonstrates proof of concept for subcutaneous microdialysis as a novel approach to monitoring metabolic wellbeing in the fetus and neonate. Although interstitial lactate reflected trends in arterial lactate in response to hypoxia and lactate infusion in neonatal piglets, further research is required to fully characterise this relationship, including standardisation of the hyperlactataemia model described here. This research has also identified a range of individual and contextual factors that influence how obstetricians use fetal monitoring technology and highlights the urgent need for future qualitative studies to improve understanding of this complex process, alongside efforts to develop new technology