Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method

Spilka, Jiří; Georgoulas, George; Karvelis, Petros; Chudáček, Václav; Stylios, Chrysostomos; Lhotská, Lenka

doi:10.1007/978-3-319-07064-3_45

Cited by 30 publications

(36 citation statements)

References 38 publications

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“…In 2014, a large CTG dataset was devised into a random forest classifier, a form of decision tree, using latent class analysis to identify normal and abnormal CTG patterns. In this study, the sensitivity and specificity of correct CTG interpretation were 72% and 78% respectively; those values were calculated based on a complex 'Aggregated Confusion Matrix model' (12). Another study in 2017, where CTG data, age of mother, pH of umbilical artery, Apgar scores, base excess and deficit were used to classify normal vaginal and caesarean section deliveries.…”

Section: Ai In Obstetricsmentioning

confidence: 97%

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Emin

Papalois

et al. 2019

In Vivo

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Section: Ai In Obstetricsmentioning

confidence: 97%

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Emin

Papalois

et al. 2019

In Vivo

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“…Besides, according to the literature [38,71,72], entropy-based features have shown better performance in classification compared with the conventional CTG signal analysis. For the computation of the entropy-based features, following [69,73,74], we employed an embedding dimension m = 2 and a tolerance r = 0.2 × σ, where σ was the standard deviation.…”

Section: Feature Computationmentioning

confidence: 99%

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

2019

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Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

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“…While, Karabulut et al [56] utilised an adaptive boosting (AdaBoost) classifier producing an accuracy of 95.01% -again no Sensitivity or Specificity values were provided. While Spilka et al, [13], used a Random Forest (RF) classifier and latent class analysis (LCA) [57] producing Sensitivity and Specificity values of 72% and 78% respectively [5]. Generating slightly better results in [45], Spilka et al attempted to detect perinatal outcomes using a C4.5 decision tree, Naive Bayes, and SVM.…”

Section: Automated Cardiotocography Classificationmentioning

confidence: 99%

“…However, several studies do argue that 50% of birthrelated brain injuries could have been prevented if CTG was interpreted correctly [12]. Conversely, there is evidence to indicate that over-interpretation increases the number of births delivered by caesarean section even when there are no known risk factors [13].…”

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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Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method

Cited by 30 publications

References 38 publications

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

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

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