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Book ChapterDOI

Comprehensive Study of Fetal Monitoring Methods for Detection of Fetal Compromise

TL;DR: In this paper, the authors proposed more accurate noninvasive fetal ECG (NIfECG) as data acquisition methods to acquire FHR and electrohysterogram (EHG) to capture UC.
Abstract: Fetal monitoring usually refers to monitoring fetal heart rate (FHR) for detection fetal well-being. This is important activity carried out by doctors during prepartum, intrapartum phase as per health requirement of patient. Fetal monitoring is required to reduce chances of fetal to become hypoxic (when fetal is deprived from sufficient oxygen) that can cause fetal brain injury and even fetal death. Fetal monitoring plays important role in reducing mortality and morbidity rate. The most common noninvasive fetal monitoring device is cardiotocograph (CTG). CTG captures FHR based on Doppler ultrasound principle and uterine contractions (UC) based on pressure transducers. The present study highlights accuracy limitations of CTG and proposes more accurate noninvasive fetal ECG (NIfECG) as data acquisition methods to acquire FHR and electrohysterogram (EHG) to capture UC. CTG interpretation is one of the decision-making parameters used by doctors for early intervention like caesarian section. CTG interpretation often suffers from inter-observer and intra-observer agreement of CTG patterns which are non-reassuring. To overcome this limitation, computerized analysis can be useful. The present study also discusses usage of machine learning to detect fetal compromise. Further, in addition to FHR and UC analysis, we also propose to use ST waveform analysis to improve results.
Citations
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Journal ArticleDOI
TL;DR: It is demonstrated that the adopted electrode configuration plays a key role in the effectiveness of BSS and guidelines for optimal electrode positioning are proposed and a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation.
Abstract: Objective. Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. Methods. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Results. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configurat- ion is recognized in 92.2% of the subjects. Conclusion. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. Significance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

1 citations

Journal ArticleDOI
TL;DR: In this article , a support vector regression model was proposed to automatically identify the best electrode configuration in terms of fetal heart rate estimation accuracy and to dynamically adjust it to varying fetal presentation.
Abstract: Objective. Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. Methods. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Results. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configurat- ion is recognized in 92.2% of the subjects. Conclusion. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. Significance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

1 citations

Book ChapterDOI
Makoto Naoi1
01 Jan 2023
Book ChapterDOI
01 Jan 2022
TL;DR: In this article , the authors proposed and assessed a method suitable for single channel based on principal component analysis (PCA) for extracting fetal ECG for accessing fetal wellbeing during antepartum and intrapartum phases.
Abstract: Fetal heart rate (FHR) monitoring is done for accessing fetal wellbeing during antepartum and intrapartum phases. Although noninvasive fetal electrocardiogram (NIfECG) is a potential data acquisition method for FHR, extraction of fetal electrocardiogram (ECG) from the abdominal ECG (aECG) is one of the major challenging research areas. This chapter proposed and assessed a method suitable for single channel based on principal component analysis (PCA) for extracting fetal ECG. Maternal R peaks and fetal R peaks were detected using Pan Tomkins algorithm (PTA) and improved Pan Tomkins algorithm (IPTA), respectively. Performance of fetal QRS detection is assessed using two open-access databases available online. The method shows satisfactory performance when compared with similar methods and makes it suitable for using a single channel system.
References
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Journal ArticleDOI
TL;DR: Improved care at birth is essential to prevent 1.3 million intrapartum stillbirths, end preventable maternal and neonatal deaths, and improve child development, and provide a way to target interventions to reach more than 7000 women every day worldwide who experience the reality of stillbirth.

1,099 citations

Journal ArticleDOI
TL;DR: A weighted kernel-based SMOTE (WK-SMOTE) that overcomes the limitation of SMOTE for nonlinear problems by oversampling in the feature space of support vector machine (SVM) classifier is proposed and shown to improve performance when compared to other baseline methods on multiple benchmark imbalanced data sets.
Abstract: Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and consist of multiple categories or classes. Learning discriminative models from such data sets is challenging due to the lack of representative data and the bias of traditional classifiers toward the majority class. Sampling methods like synthetic minority oversampling technique (SMOTE) have been traditionally used for such problems to artificially balance the data set before being trained by a classifier. This paper proposes a weighted kernel-based SMOTE (WK-SMOTE) that overcomes the limitation of SMOTE for nonlinear problems by oversampling in the feature space of support vector machine (SVM) classifier. The proposed oversampling algorithm along with a cost-sensitive SVM formulation is shown to improve performance when compared to other baseline methods on multiple benchmark imbalanced data sets. In addition, a hierarchical framework is developed for multiclass imbalanced problems that have a progressive class order. The proposed WK-SMOTE and hierarchical framework are validated on a real-world industrial fault detection problem to identify deterioration in insulation of high-voltage equipments.

194 citations

Journal ArticleDOI
TL;DR: The development and clinical validation of a computer system for the numerical analysis of nonstress tests is reviewed, and recent improvements are reported.

166 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared all three methods of contraction detection simultaneously in laboring women, including Tocodynamometry, EHG, and intrauterine pressure catheter (IUPC).

154 citations

Journal ArticleDOI
TL;DR: In this article, the authors present guidelines and recommendations concerning CTG and ST waveform interpretation and classification for intrapartum surveillance of fetal electrocardiogram (ECG) during labour.

124 citations