Machine learning on cardiotocography data to classify fetal outcomes: A scoping review

Introduction: Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation. Materials and method: We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria. Results: We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study. Conclusion: ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.</p

Different aspects of electronic fetal monitoring during labor

Background: Cardiotocography (CTG) is a tool to assess fetal well-being during labor and to detect early signs of fetal distress and thereby enable timely interventions to reduce neonatal morbidity and mortality. CTG is associated with shortcomings; poor reliability in interpretation, low specificity with a high proportion of false positive tracings indicating fetal distress when not accurate, no proven effect on rare severe outcomes such as mortality and cerebral palsy, but rather contributing to an increased risk of operative delivery. The aims of this thesis was to determine I) if an extended CTG education could lead to better reliability in interpretation compared to a national standard education, II) if a computerized algorithm could be developed with precision in detecting and quantitating decelerations on CTG, III) if deceleration area was a better predictor of fetal acidemia during labor than deceleration depth and duration, IV) the proportion of fetuses with undetected small for gestational age (SGA) in a low-risk population, comparing women that present with normal CTG at admission to labor (admCTG) to those with abnormal admCTG and to compare neonatal outcomes in the two groups stratified on SGA or non-SGA. Material and methods: The CTG tracings used in paper I-III were extracted from a previous cohort of women in labor, from Karolinska University Hospital, Sweden. All women had undergone fetal blood sampling (FBS) during labor due to suspicious CTG patterns. Six obstetricians from two different hospitals were used as observers in paper I. Inter- and intra-observer reliability using Cohen’s and Fleiss kappa was determined for different parameters assessed on CTG. In paper II two obstetricians visually analyzed CTG tracings with variable decelerations and specified duration, depth and area for each deceleration. The computerized algorithm analyzed and quantified the same CTG traces and was compared to the observers using intra-class correlation and Bland-Altman analysis. In paper III the predictive value of deceleration area, duration, and depth for fetal acidemia, measured as lactate concentration at FBS, was explored using receiver operating characteristics, area under curve (ROC AUC). In paper IV, a register-based study, the risk of SGA in relation to the result of admCTG, normal vs abnormal was assessed in low-risk pregnancies. Neonatal outcomes were also determined by multiple logistic regression analysis. Results: I) The inter- and intra-observer reliability was moderate to excellent at both departments, kappa 0.41-0.93. The department with extended education reached significantly higher interobserver agreement for two of six CTG parameters assessed. II) Computerized assessment of decelerations on CTG compared to visual observers reached excellent intraclass correlation (0.89-0.95) and low bias in Bland-Altman analysis, comparable to that between the two observers. III) The deceleration measures with the best prediction of fetal acidemia was cumulative deceleration area and duration, ROC AUC 0.682 and 0.683 respectively compared to deceleration depth 0.631. IV) The proportion of SGA was two-fold higher among neonates presenting with abnormal admCTG (18.6%) compared to normal admCTG (9.7%). The risk of composite severe adverse neonatal complications was substantially higher in the group with abnormal admCTG/SGA compared to normal admCTG/non-SGA, adjusted odds ratio 23.7 (95% confidence interval 9.8-57.3) Conclusion: Inter- and intra-observer agreement was better than expected at both departments studied and extended education might have an impact on interpretation reliability. A novel computerized algorithm for CTG assessment has high precision in detecting and quantifying decelerations. Cumulative deceleration area and duration are better predictors of fetal acidemia than deceleration depth. In presumed low-risk pregnancies there is a group of undetected SGA fetuses that more often present with abnormal admCTG and are at higher risks of neonatal complications

Non-invasive procedure for fetal electrocardiography

Antenatal fetal surveillance is a field of increasing importance in modern obstetrics. Measurements extracted (such as fetal heart rate) from antenatal fetal monitoring techniques have the potential to reduce the social, personal and financial burdens of fetal death on families, health care systems and the community. Techniques to monitor the fetus through pregnancy have been developed with the aim of providing information to enable the clinician to diagnose fetal wellbeing, characterise development and detect abnormality. An early diagnosis before delivery may increase the effectiveness of the appropriate treatment. Over the years, various research efforts have been carried out in the field of fetal electrocardiography by attaching surface electrodes to the maternal body. Unfortunately the desired fetal heartbeat signals at the electrode output are buried in an additive mixture of undesired interference disturbances. In this thesis, a non-invasive fetal electrocardiogram machine has been designed, constructed and implemented. This machine is composed of three modified electrocardiogram circuits and an external soundcard. Data was acquired from four surface electrodes placed on the maternal body. Eleven pregnant subjects, with a gestation age between the 30th and 40th weeks of pregnancy, were used to investigate the validity of this machine. Fetal R-waves were detected in 72.7 percent of subjects. The development of a non-invasive machine, capable of detecting and recording valuable anatomic and electrophysiological information of a fetus, represents an important tool in clinical and investigative obstetrics

Long-term outcome after hypothermia-treated hypoxic-ischaemic encephalopathy

Hypoxic-ischaemic encephalopathy (HIE) is a major cause of acquired brain injury in newborn infants. It is a potentially life-threatening condition that leaves survivors at substantial risk of life-long debilitating sequelae including cerebral palsy, epilepsy, intellectual disability, sensory disruption, behavioural issues, executive difficulties and autism spectrum disorder. More subtle cognitive impairments are common among survivors free of major neuromotor disability. Therapeutic hypothermia (TH) reduces the risk of death and disability in nearterm/term new-born infants with moderate and severe HIE. Outcomes in adolescence and adulthood following HIE treated with TH are not yet known. The majority of infants with HIE also suffer multi-organ dysfunction resulting from the hypoxic-ischaemic insult. The kidneys are particularly sensitive to hypoxia-ischaemia, with up to 72% of asphyxiated infants suffering acute kidney injury (AKI) prior to the advent of TH. Evidence point to AKI being independently associated with increased neonatal morbidity and mortality. To date, very little is known about long-term renal consequences following neonatal AKI in asphyxiated infants treated with TH. The overall aim of this thesis was to contribute to the improved treatment and care of infants with HIE by means of increased knowledge about the predictive value of early aEEG, neonatal AKI, and long-term outcomes in the era of TH. In a small population-based cohort, the predictive value of early amplitude-integrated EEG (aEEG) was demonstrated to be altered in infants treated with TH due to HIE. Poor outcome at the age of 1 year was only seen among infants with a persisting abnormal aEEG background pattern at and beyond 24 hours of age. In a population-based, prospective, longitudinal study including all children treated with TH between 2007 and 2009 in Stockholm, Sweden, we assessed neuromotor, neurologic, cognitive and behavioural outcomes at 6-8 and 10-12 years of age. Seventeen per cent of survivors developed CP. Survivors free of major neuromotor impairment had cognitive abilities within normal range. Repeated assessment in early adolescence revealed new deficits in 26% of children with previously favourable outcome. The proportion of children with executive difficulties in this patient population appears to be higher than in the general population. Outcomes in children with a history of moderate HIE remain heterogenous also in the era of TH. In a population-based cohort of all children treated with TH between 2007 and 2009 in Stockholm, Sweden, 45% suffered neonatal AKI. Severe AKI necessitating kidney support therapy was rare. Among infants with AKI, 20% fulfilled only the urinary output criterion of the neonatal modified KDIGO (Kidney Disease Improving Global Outcomes) definition. Mortality was higher among infants with AKI. At 10-12 years of age, 21% of children had decreased glomerular filtration rate (GFR) estimated from creatinine with the Schwartz-Lyon equation. A more in-depth assessment of renal functions in the above-mentioned population-based cohort demonstrated that renal sequelae (defined as decreased GFR, albuminuria, hypertension or normal high blood pressure, reduced renal volume on magnetic resonance imaging, or elevated Fibroblast Growth Factor 23) were rare at 10-12 years of age following perinatal asphyxia and TH. The Schwarz-Lyon equation appears to underestimate GFR in this patient population

Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

Electrohysterography in the diagnosis of preterm birth: a review

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aaad56.[EN] Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. Objective: A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. Approach: This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. Main results: Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. Significance: This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grant DPI2015-68397-R.Garcia-Casado, J.; Ye Lin, Y.; Prats-Boluda, G.; Mas-Cabo, J.; Alberola Rubio, J.; Perales Marin, AJ. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement. 39(2). https://doi.org/10.1088/1361-6579/aaad56S39

B-Type Natriuretic Peptide and Novel Cardiac Ultrasound in Very Preterm Neonates: Potential Markers for the Detection of Pulmonary Hypertension and for Risk Stratification

Very premature birth causes an interruption of cardiorespiratory development. Successful pulmonary adaptation requires synchronous development of alveoli and pulmonary capillary networks. Very premature birth threatens this process. Exposure to excess oxygen, combined with the modifying effects of growth and inflammation, can cause bronchopulmonary dysplasia characterised by maldevelopment of alveoli and pulmonary vasculature. This may be complicated by pulmonary hypertension causing right heart dysfunction which may persist to adulthood. As pulmonary hypertension develops insidiously, and is associated with significant morbidity and mortality, screening is recommended. However, there are no consensus diagnostic criteria and a paucity of validated heart ultrasound parameters in the very preterm population. N-Terminal proB-type Natriuretic Peptide (NTproBNP), a cardiac hormone, is a promising biomarker as it is a sensitive marker of ventricular wall pressure and volume stress and predicts pulmonary hypertension in other settings. There is limited data in very preterm infants. Premature infants have unstable oxygen saturations due to multifactorial causes which may be exacerbated by pulmonary hypertension. Oxygen saturation targeting attempts to reduce hypoxia and hyperoxia. Modern oximeters allow detailed analysis of oxygen saturation patterns but optimal measures of instability have not been established. We investigated temporal changes in NTproBNP and factors influencing levels, in a cohort of very preterm infants. We evaluated NTproBNP as a biomarker for pulmonary hypertension by pairing NTproBNP with heart ultrasound and pulse oximetry data in the neonatal period and report clinical and neurodevelopmental outcomes at two years. Quantitative measures of right heart function and compliance with oxygen saturation alarm limits in our unit were evaluated. Current screening recommendations were reviewed. We demonstrated high NTproBNP on days 3 and 10 then decreasing NTproBNP beyond the period of cardiac transition. NTproBNP was a highly sensitive and specific marker of haemodynamically significant patent ductus arteriosus and a modest predictor of severe bronchopulmonary dysplasia. None of our cohort had evidence of pulmonary hypertension at 36 weeks post menstrual age by predefined conventional heart ultrasound criteria. However, we demonstrated it is feasible for more advanced quantitative measures of right heart function to be incorporated into clinician performed heart ultrasound that may be of value to screening programmes. We showed changes in these parameters over time, reviewed their reliability, and investigated differences in infants with and without bronchopulmonary dysplasia. Oxygen saturation instability increased in our cohort over time peaking at day 28 and was greater in infants with bronchopulmonary dysplasia. We identified oxygen saturation coefficient of variation and percentage time less than 88% as useful measures of instability. Compliance with oxygen saturation alarm limits in our unit was low despite high levels of nursing experience potentially exposing extremely premature infants to iatrogenic hyperoxia which may contribute to pulmonary hypertension. In summary, we found a lower than expected rate pulmonary hypertension in our very preterm cohort. There is insufficient evidence for NTproBNP as a standalone biomarker for pulmonary hypertension but it may be useful as an adjunct to comprehensive heart ultrasound evaluation. To reduce pulmonary hypertension, surveillance of at risk infants and oxygen saturation stewardship is needed

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being