Intrapartum fetal surveillance

Comparative study of neonatal brain injury fetuses using machine learning methods for perinatal data

OBJECTIVE

CTG is used to record the fetus's fetal heart rate and uterine contraction signal during pregnancy. The prenatal fetal intrauterine monitoring level can be used to evaluate the fetal intrauterine safety status and reduce the morbidity and mortality of the perinatal fetus. Perinatal asphyxia is the leading cause of neonatal hypoxic-ischemic encephalopathy and one of the leading causes of neonatal death and disability. Severe asphyxia can cause brain and permanent nervous system damage and leave different degrees of nervous system sequelae.

METHODS

This paper evaluates the classification performance of several machine learning methods on CTG and provides the auxiliary ability of clinical judgment of doctors. This paper uses the data set on the public database UCI, with 2126 samples.

RESULTS

The accuracy of each model exceeds 80%, of which XGBoost has the highest accuracy of 91%. Other models are Random tree (90%), light (90%), Decision tree (83%), and KNN (81%). The performance of the model in other indicators is XGBoost (precision: 90%, recall: 93%, F1 score: 90%), Random tree (precision: 88%, recall: 91%, F1 score: 89%), lightGBM (precision: 87%, recall: 93%, F1 score: 90%), Decision tree (precision: 83%, recall: 86%, F1 score: 84%), KNN (precision: 77%, recall: 85%, F1 score: 81%).

CONCLUSION

The performance of XGBoost is the best of all models. This result also shows that using the machine learning method to evaluate the fetus's health status in CTG data is feasible. This will also provide and assist doctors with an objective assessment to assist in clinical diagnosis.

A CNN-RNN unified framework for intrapartum cardiotocograph classification

BACKGROUND AND OBJECTIVE

Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is very vital for pregnant women before delivery. During pregnancy, it is crucial to judge whether the fetus is abnormal, which helps obstetricians carry out early intervention to avoid fetal hypoxia and even death. At present, clinical fetal monitoring widely used fetal heart rate monitoring equipment. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are important information to evaluate fetal health status.

METHODS

This paper is based on 1D-CNN (One Dimension Convolutional Neural Network) and GRU (Gate Recurrent Unit). We preprocess the obtained data and enhances them, to make the proportion of number of instances in different class in the training set is same.

RESULTS

In model performance evaluation, standard evaluation indicators are used, such as accuracy, sensitivity, specificity, and ROC (receiver operating characteristic). Finally, the accuracy of our model in the test set is 95.15%, the sensitivity is 96.20%, and the specificity is 94.09%.

CONCLUSIONS

In fetal heart rate monitoring, this paper proposes a 1D-CNN and bidirectional GRU hybrid models, and the fetal heart rate and uterine contraction signals given by monitoring are used as input feature to classify the fetal health status. The results show that our approach is effective in evaluating fetal health status and can assists obstetricians in clinical decision-making. And provide a baseline for the introduction of 1D-CNN and bidirectional GRU hybrid models into the evaluation of fetal health status.

Fully Automatic Classification of Cardiotocographic Signals with 1D-CNN and Bi-directional GRU

Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is vital for pregnant women before delivery. During pregnancy, it is essential to classify whether the fetus is abnormal, which helps physicians carry out early intervention to avoid fetal heart hypoxia and even death. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are essential to estimate fetal health status. In this paper, we pre-process the obtained data set and enhance them using Hermite interpolation on the abnormal classification in the samples. We use the 1D-CNN and GRU hybrid models to extract the abstract features of fetal heart rate and uterine contraction signals. Several evaluation metrics are used for evaluation, and the accuracy is 96 %, while the sensitivity is 95 %, and the specificity is 96 %. The experiments show the effectiveness of the proposed method, which can provide physicians and users with more stable, efficient, and convenient diagnosis and decision support.

Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment

Cardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. In this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.

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

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.

The shifting sands of medico-legal intra-partum Ctg (I-P Ctg) monitoring

Intra-partum (IP) surveillance of the unborn child by cardiotacography (CTG) monitoring is the commonest obstetric procedure in the developed world.(1) It is also the most medico-legally contested obstetric procedure in labour. In 2011, 'birth asphyxia' comprised 50% of the UK National Health Service (NHS) litigation costs,(2) and in the 2000-2010 decade, the same NHS paid out £3.1 billion for maternity medico-legal claims (the highest of any speciality), mostly involving cerebral palsy and CTG misinterpretation.(3) This article looks at a number of characteristics of IP CTG monitoring which argue for its questionable solidity of base in court proceedings.