Human Fetus Health Classification on Cardiotocographic Data Using Random Forests

Peterek, Tomáš; Gajdoš, Petr; Dohnálek, Pavel; Krohová, Jana

doi:10.1007/978-3-319-07773-4_19

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…Ensemble classifiers have also shown promising performance in similar studies (Tomas et al, 2013 ; Peterek et al, 2014 ). Therefore, we also consider the AdaBoost classifier (Schapire, 1999 ) in this study as a representation of ensemble classifiers.…”

Section: Features Processingmentioning

confidence: 83%

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

et al. 2019

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We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

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Section: Features Processingmentioning

confidence: 83%

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

et al. 2019

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“…Model training based on the convolution neural network algorithm MKNet will be used.The FHR feature field, which represents the medical characteristic value analyzed from the fetal heart rate curve [22]. Support vector machines and random forests will be used for model training [9]. …”

Section: Methodsmentioning

confidence: 99%

“…This paper will setup the control group and the experimental group. The control group adopts the traditional data classification methods, including the support vector machine (SVM) and the random forest (RF) method [9]. The experimental group adopts an artificial neural network (ANN) method, including convolution neural networks (CNN) and recurrent neural network (RNN) methods.…”

Section: Introductionmentioning

confidence: 99%

The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network

Tang

Wang

et al. 2018

Computational and Mathematical Methods in Medicine

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Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.

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“…In this study, we consider the Random Forest (RF) classifier [23], [74]. This algorithm uses an ensemble of many randomised decision-trees to vote on the classification outcome.…”

Section: Ensemble Classifiers Have Shown To Have Powerful Classificatmentioning

confidence: 99%

“…Computerised CTG has played a significant role in developing objective measures as a function of CTG signals [14], particularly within the machine learning community [15]- [18], [7], [12], [19]- [23]. According to a Cochrane report in 2015, computerised interpretation of CTG traces significantly reduced perinatal mortality [24].…”

Section: Introductionmentioning

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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Human Fetus Health Classification on Cardiotocographic Data Using Random Forests

Cited by 14 publications

References 9 publications

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network

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

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