Computerised Interpretation Systems for Cardiotocography for Both Home and Hospital Uses

Lu, Yu-Shiang; Gao, Yongjie; Xie, Yuyang; He, Shu-Nan

doi:10.1109/cbms.2018.00080

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In recent years, communities and families are greatly entering into the comprehensive application of information platforms such as cloud computing technology, IoT healthcare, big data, communication technology, etc. It provides individuals with purposeful and personalized services, enabling them to advance from telemedicine to new ideas and ways of preventing disease and addressing major public health issues in the lives of women and children [11][12][13]. The IoT [14] means things are connected via the Internet that, understands the communication between objects using modern information technologies such as smart sensing, identification technology, and wireless communication.…”

Section: Introductionmentioning

confidence: 99%

Ambulatory Monitoring of Maternal and Fetal using Deep Convolution Generative Adversarial Network for Smart Health Care IoT System

Srinivasan¹

2022

IJACSA

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With the increase in the number of high-risk pregnancies, it is important to monitor the health of the fetus during pregnancy. Major advances in the field of study have led to the development of intelligent automation systems that enable clinicians to predict and determine the monitoring of Maternal and Fetal Health (MFH) with the aid of the Internet of Things (IoT). This paper provides a solution for monitoring high-risk MHF based on IoT sensors, data analysis-based feature extraction, and an intelligent system based on the Deep Convolutional Generative Adversarial Network (DCGAN) classifier. Various clinical indicators such as heart rate of MF, oxygen saturation, blood pressure, and uterine tonus of maternal are monitored continuously. Many data sources produce large amounts of data in different formats and ratios. The smart health analytics system proposes to extract several features and measure linear and non-linear dimensions. Finally, a DCGAN has been proposed as a predictive mechanism for the simultaneous classification of MFH status by considering more than four possible outcomes. The results showed that the proposed system for mobile monitoring between MFH is a practical solution based on the IoT.

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Section: Introductionmentioning

confidence: 99%

Ambulatory Monitoring of Maternal and Fetal using Deep Convolution Generative Adversarial Network for Smart Health Care IoT System

Srinivasan¹

2022

IJACSA

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“…Using effective machine learning (ML) classification methods for classifying the CTG patterns may increase www.ijacsa.thesai.org significantly the performance of predicting the fetal state [13]. Moreover, selecting the appropriate values for the parameters of ML methods is considered as one of the factors that effect on the success of ML-based applications on the various medical data samples [13,14]. Where, if these parameters have inappropriate values then the classification process will be more difficult during training to get optimal or near optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Single Layer Perceptron-based Approach for Cardiotocography Data Classification

Alkhamees¹

2022

IJACSA

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Uterine Contractions (UC) and Fetal Heart Rate (FHR) are the most common techniques for evaluating fetal and maternal assessment during pregnancy and detecting the changes in fetal oxygenation occurred throughout labor. By monitoring the Cardiotocography (CTG) patterns, doctors can measure fetus state, accelerations, heart rate, and uterine contractions. Several computational and machine learning (ML) methods have been done on CTG recordings to improve the effectiveness of fetus analysis and aid the doctors to understand the variations in their interpretation. However, getting an optimal solution and best accuracy remains an important concern. Among the various ML approaches, artificial neural network (ANN)-based approach has achieved a high performance in several applications. In this paper, an optimized Single Layer Perceptron (SLP)-based approach is proposed to classify the CTG data accurately and predict the fetal state. The approach is able to exploit the advantages of SLP model and optimize the learning rate using a grid search method in which we can arrive at the best accuracy and converge to a local minima. The approach is evaluated on CTG dataset of University of California, Irvine (UCI). The optimized SLP model is trained and tested on the dataset using a 10-fold cross-validation technique to classify the CTG patterns as normal, suspect or pathologic. The experimental results show that the proposed approach achieved 99.20% accuracy compared with the state-of-the-art models.

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Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning

Chen

et al. 2020

Artificial Intelligence in Medicine

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Computerised Interpretation Systems for Cardiotocography for Both Home and Hospital Uses

Cited by 3 publications

References 11 publications

Ambulatory Monitoring of Maternal and Fetal using Deep Convolution Generative Adversarial Network for Smart Health Care IoT System

Ambulatory Monitoring of Maternal and Fetal using Deep Convolution Generative Adversarial Network for Smart Health Care IoT System

An Optimized Single Layer Perceptron-based Approach for Cardiotocography Data Classification

Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning

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