Doppler‐based fetal heart rate analysis markers for the detection of early intrauterine growth restriction

Stroux, Lisa; Redman, Christopher W.G.; Georgieva, Antoniya; Payne, Stephen J.; Clifford, Gari D.

doi:10.1111/aogs.13228

Cited by 19 publications

(21 citation statements)

References 31 publications

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“…This selection was relevant as little research has used this type of features for assessing fetal maturity. Finally, the STV/LTV ratio, which previously has shown to be relevant for detecting IUGR cases (Stroux et al, 2017), was also relevant for GA estimation.…”

Section: Interpretations Of Findingsmentioning

confidence: 82%

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Valderrama

Marzbanrad

Hall-Clifford

et al. 2020

Front. Artif. Intell.

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In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low-and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

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Section: Interpretations Of Findingsmentioning

confidence: 82%

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Valderrama

Marzbanrad

Hall-Clifford

et al. 2020

Front. Artif. Intell.

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“…Their data showed a lower percentage of high variability (active sleep) episodes in fetuses with IUGR compared to controls. In particular, these findings were more pronounced in the gestations ≤34 weeks, where reduced LTV during active sleep states showed an AUC of 74% (70–78%) for distinguishing fetuses with IUGR from AGA fetuses [44].…”

Section: Discussionmentioning

confidence: 99%

A systematic review on the utility of non-invasive electrophysiological assessment in evaluating for intra uterine growth restriction

Smith

Nair

Warty

et al. 2019

BMC Pregnancy Childbirth

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Background Non-invasive electrophysiological assessment (NIEA) is an evolving area in fetal surveillance and is attracting increasing research interest. There is however, limited data outlining its utility in evaluating intra uterine growth restriction (IUGR). The objective of this study was to carry out a systematic review to outline the utility of NIEA parameters in evaluating IUGR. Methods A systematic review of peer reviewed literature was performed, searching PUBMED, Ovid MEDLINE and EMBASE. The outcomes of interest included NIEA parameters [P wave duration, PR interval, QRS duration, QT interval, T/QRS ratio, short term variability (STV) and long term variability (LTV)] and a descriptive summary of relevant studies as well. Results Sixteen studies were identified as suitable for inclusion. The utility of NIEA parameters were investigated in tabular form. In particular, QRS and QT duration, T/QRS ratio, STV and PRSA analysis displayed utility and merit further consideration in evaluating for IUGR. Issues identified in the review were in relation to variances in definition of IUGR, small sample sizes and the lack of technological consistency across studies. Conclusion NIEA shows promise as an adjunct surveillance tool in fetal diagnostics for IUGR. Larger prospective studies should be directed towards establishing reliable parameters with a focus on uniformity of IUGR definition, technological consistency and the individualisation of NIEA parameters. Electronic supplementary material The online version of this article (10.1186/s12884-019-2357-9) contains supplementary material, which is available to authorized users.

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“…The presence of both STV and LTV describe normal cardiac function [30]. If STV or LTV decreases or is absent, this could indicate the onset of an adverse pathological outcome [31].…”

Section: Cardiotocography Feature Extractionmentioning

confidence: 99%

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces

Fergus

Selvaraj

Chalmers

2018

Computers in Biology and Medicine

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Human visual inspection of Cardiotocography traces is used to monitor the foetus during labour and avoid neonatal mortality and morbidity. The problem, however, is that visual interpretation of Cardiotocography traces is subject to high inter and intra observer variability. Incorrect decisions, caused by miss-interpretation, can lead to adverse perinatal outcomes and in severe cases death. This study presents a review of human Cardiotocography trace interpretation and argues that machine learning, used as a decision support system by obstetricians and midwives, may provide an objective measure alongside normal practices. This will help to increase predictive capacity and reduce negative outcomes. A robust methodology is presented for feature set engineering using an open database comprising 552 intrapartum recordings. State-of-the-art in signal processing techniques is applied to raw Cardiotocography foetal heart rate traces to extract 13 features. Those with low discriminative capacity are removed using Recursive Feature Elimination. The dataset is imbalanced with significant differences between the prior probabilities of both normal deliveries and those delivered by caesarean section. This issue is addressed by oversampling the training instances using a synthetic minority oversampling technique to provide a balanced class distribution. Several simple, yet powerful, machine-learning algorithms are trained, using the feature set, and their performance is evaluated with real test data. The results are encouraging using an ensemble classifier comprising Fishers Linear Discriminant Analysis, Random Forest and Support Vector Machine classifiers, with 87% (95% Confidence Interval: 86%, 88%) for Sensitivity, 90% (95% CI: 89%, 91%) for Specificity, and 96% (95% CI: 96%, 97%) for the Area Under the Curve, with a 9% (95% CI: 9%, 10%) Mean Square Error.

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Doppler‐based fetal heart rate analysis markers for the detection of early intrauterine growth restriction

Cited by 19 publications

References 31 publications

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

A systematic review on the utility of non-invasive electrophysiological assessment in evaluating for intra uterine growth restriction

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces

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