Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost

Zhang, Yang; Zhao, Zhidong

doi:10.1109/cisp-bmei.2017.8302314

Cited by 45 publications

(35 citation statements)

References 13 publications

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“…In [16], the authors propose a new method for selecting a subset of optimal functions to increase the accuracy using pathological classification of the CTG. Feature selection performed with PCA as a pre-processing stage in machine training has proven to be very effective in improving computational time and accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Dimensionality Reduction Techniques on Big Data

et al. 2020

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Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results. INDEX TERMS Cardiotocography dataset, dimensionality reduction, feature engineering, linear discriminant analysis, machine learning, principal component analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Dimensionality Reduction Techniques on Big Data

et al. 2020

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“…Medical decision making relies heavily on automatic analysis of medical data, this greatly helps improve diagnosis and medical care ( Das, Roy & Saha, 2015a ). Cardiotocography is widely used by doctors to obtain detailed physiological information from fetuses and pregnant women as a technique for diagnosing fetal well-being ( Zhang & Zhao, 2017 ). In article ( Fergus, Selvaraj & Chalmers, 2018 ), automated computer analysis of cardiotocography signals showed strong evidence in diagnosing actual perinatal complications and predicted the onset of pathological results with far less variability and better accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Generally, cardiotocography is a practical method that can be used to examine fetal well-being and at the same time widely used for intrapartum and antepartum fetal monitoring ( Nagendra et al, 2017 ; Zhang & Zhao, 2017 ; Das, Roy & Saha, 2015b ). Cardiotocography (CTG) is also called Electronic Fetal Monitoring (EFM), EFM was first introduced around 1960, and became the first tool to use phonocardiography to record Fetal Heart Rate (FHR), and was then replaced by a doppler signal with significant improvements to signal quality ( Pinas & Chandraharan, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

Al-Yousif

Jaenul²,

Al-Dayyeni

et al. 2021

PeerJ Computer Science

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Context The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. Objective This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG. Methods Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers. Results After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles). Discussion This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques. Conclusions This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.

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“…As a brand-new field that has been developing rapidly for more than a decade, DL has received increasing attention from researchers, and it has obvious advantages over shallow models (i.e., ML) in feature engineering and model building ( Zhang and Zhao, 2017 ). DL is good for excavating an increasing number of abstract features that possess better generalization ability from the original input data, especially the image ( Schmidhuber, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…Especially, heart rate variability (HRV) has become a valuable resource in FHR signal characterization ( Romano et al, 2018 ). On the other hand, since not all extracted features are valuable for classification, feature selection algorithms and dimensional reduction methods have been utilized to select an optimal feature set to improve the performance, including a genetic algorithm (GA) ( Xu et al, 2014 ), information gain (IG) ( Spilka et al, 2012 ), and principal component analysis (PCA) ( Zhang and Zhao, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network

et al. 2019

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Background: Electronic fetal monitoring (EFM) is widely applied as a routine diagnostic tool by clinicians using fetal heart rate (FHR) signals to prevent fetal hypoxia. However, visual interpretation of the FHR usually leads to significant inter-observer and intra-observer variability, and false positives become the main cause of unnecessary cesarean sections.Goal: The main aim of this study was to ensure a novel, consistent, robust, and effective model for fetal hypoxia detection.Methods: In this work, we proposed a novel computer-aided diagnosis (CAD) system integrated with an advanced deep learning (DL) algorithm. For a 1-dimensional preprocessed FHR signal, the 2-dimensional image was transformed using recurrence plot (RP), which is considered to greatly capture the non-linear characteristics. The ultimate image dataset was enriched by changing several parameters of the RP and was then used to feed the convolutional neural network (CNN). Compared to conventional machine learning (ML) methods, a CNN can self-learn useful features from the input data and does not perform complex manual feature engineering (i.e., feature extraction and selection).Results: Finally, according to the optimization experiment, the CNN model obtained the average performance using optimal configuration across 10-fold: accuracy = 98.69%, sensitivity = 99.29%, specificity = 98.10%, and area under the curve = 98.70%.Conclusion: To the best of our knowledge, this approached achieved better classification performance in predicting fetal hypoxia using FHR signals compared to the other state-of-the-art works.Significance: In summary, the satisfied result proved the effectiveness of our proposed CAD system for assisting obstetricians making objective and accurate medical decisions based on RP and powerful CNN algorithm.

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Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost

Cited by 45 publications

References 13 publications

Analysis of Dimensionality Reduction Techniques on Big Data

Analysis of Dimensionality Reduction Techniques on Big Data

A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network

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