Polynomial FLANN Classifier for Fetal Cardiotocography Monitoring

Haweel, Mohammad T.; Zahran, O.; El‐Samie, Fathi E. Abd

doi:10.1109/nrsc52299.2021.9509832

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…At the end of the experiment, they found a classification accuracy of 99.74%. They compared the performance of the PNN classifier with functional link artificial neural network (FLANN) classifiers such as a Legendre neural network (LNN) and a Volterra neural network (VNN) and found that the PNN classifier provided better performance [30].…”

Section: Discussionmentioning

confidence: 99%

Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning

Kuzu

SANTUR

2023

Diagnostics

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(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer’s output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning.

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Section: Discussionmentioning

confidence: 99%

Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning

Kuzu

SANTUR

2023

Diagnostics

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“…Morphological features are the significant indicators to ascertain fetal state in clinical practice. Obstetricians have attempted to identify specific FHR patterns that can be seen visually as morphological features ( Haweel et al, 2021 ). Baseline, acceleration, deceleration, and variability in short and long terms represent the gross features of the FHR patterns ( Cömert et al, 2018a ).…”

Section: Methodsmentioning

confidence: 99%

A deep feature fusion network for fetal state assessment

Xiao

Liu

et al. 2022

Front. Physiol.

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CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features’ diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal’s temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method’s effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works.

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“…Haweel et al worked on a probabilistic neural network (PNN) based method for fetal condition classification (52). They compared the performance of their proposed PNN classifier with legendre neural network (LNN) and volterra neural network (VNN) classifiers.…”

Section: Dwivedi Et Al Proposed Thementioning

confidence: 99%

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

Kuzu,

Santur

2023

TUSEB

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Obstetricians commonly use Non-Stress Test to evaluate fetal well-being during the antepartum and intrapartum periods by measuring fetal heart rate and uterine contractions of the mother. Non-Stress Test is also used to diagnose fetal distress at an early stage. Early diagnosis and treatment can increase the fetus's survival rate and improve their quality of life. The fetal heart rate and uterine contraction signals obtained from the Non-Stress Test are recorded on a paper called a trace. Obstetricians interpret the trace to make decisions about the fetus's condition. However, traditional analysis of Non-Stress Test takes time, and there are differences in interpretation among experts. Newly qualified doctors and midwives are more prone to making mistakes and incorrect decisions. To overcome the differences in the interpretation of Non-Stress Test analysis and to automate the process to minimize diagnostic errors, machine learning and deep learning models have been increasingly used in recent years. In this study, the literature of the past five years is researched, and numerical expressions, tables, and graphs related to this are presented.

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Polynomial FLANN Classifier for Fetal Cardiotocography Monitoring

Cited by 5 publications

References 17 publications

Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning

Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning

A deep feature fusion network for fetal state assessment

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

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