Bidirectional Long Short-term Memory-based Intelligent Auxiliary Diagnosis of Fetal Health

Zhang, Yefei; Deng, Yanjun; Zhang, Xiaohong; Shao, Lihuan; Zhao, Zhidong

doi:10.1109/tensymp52854.2021.9550851

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In [ 38 , 41 ], DenseNet is reported to exploit dense concatenation blocks for feature mapping, but the heavy processing time makes them unsuitable for clinical settings. Multilayer perceptron and long short-term memory (LSTM) networks [ 39 , 40 ] are characterized by several layers of input nodes connected as a directed graph with the output. They both include a very dense web of parameters, resulting in redundancy and inefficiency.…”

Section: Discussionmentioning

confidence: 99%

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

HUSSAIN

Rehman

Othman

et al. 2022

Sensors

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Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.

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Section: Discussionmentioning

confidence: 99%

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

HUSSAIN

Rehman

Othman

et al. 2022

Sensors

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“…For example, only about 7% of CTU-UHB traces fall under compromised cases when a pH threshold of 7.05 is used as the outcome measure. Several approaches like under-sampling the majority class [ 117 ], oversampling the minority class [ 35 ] and using class weights in the loss function [ 37 ] are used to tackle the disproportion in classes. Under-sampling is usually not desirable as it may result in the loss of important data [ 118 ].…”

Section: Automated Fetal Compromise Classification Methodsmentioning

confidence: 99%

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

Mendis,

Palaniswami,

Brownfoot

et al. 2023

Bioengineering

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The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.

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Review on Fetal Health Classification

Nagabotu,

Namburu

2023

Algorithms for Intelligent Systems

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Bidirectional Long Short-term Memory-based Intelligent Auxiliary Diagnosis of Fetal Health

Cited by 3 publications

References 11 publications

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

Review on Fetal Health Classification

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