Cardiotocography signals with artificial neural network and extreme learning machine

Cömert, Zafer; Kocamaz, Adnan Fatih; Güngör, Sami

doi:10.1109/siu.2016.7496034

Cited by 37 publications

(21 citation statements)

References 11 publications

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“…Artificial neural network (ANN) is a computational technique inspired by the learning and generalization ability of the human brain. ANN is used for many purposes, such as function convergence, pattern recognition, and classification in many fields of science [20]. From a technical point of view, ANN consists of an input layer, one or more hidden layer(s), and an output layer [21].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community

Cömert¹,

Kocamaz²

2016

IJCA

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Cardiotocography (CTG) is a fetal monitoring technique used to determine the distress level of the fetus during pregnancy and delivery. CTG consists of two different signals including fetal heart rate (FHR) and uterine contraction (UC) activities. The linear features of FHR are the most powerful prognostic indices to ascertain whether the fetus in distress. In addition, it is observed that nonlinear features have produced very great results on the time series analysis in recently. In this context, the classification success of the neural network community designed based on the linear and nonlinear features of FHR is analyzed for the delivery process evaluated in three stages. The experimental results have shown that the system designed to distinguish normal and pathological instances is achieved the best classification accuracy at the first stage of the analysis. Also, the greatest contribution of nonlinear features to the classification accuracy is observed at the second stage of the delivery. Consequently, 92.40%, 83.29%, and 79.22% of accuracy obtained for the three stage of the analysis, respectively.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community

Cömert¹,

Kocamaz²

2016

IJCA

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“…The reliability criteria group (stage 4/ subclass 2) as reported in the articles ( Cömert, Kocamaz & Güngör, 2016 ; Magenes et al, 2016 ; Nagendra et al, 2017 ; Sahin & Subasi, 2015 ; Rei et al, 2015 ; Di Tommaso et al, 2013 ; Cömert & Kocamaz, 2017a ; Gamboa et al, 2017 ; Chen et al, 2014 ; Arif, 2015 ; Garabedian et al, 2017 ; Pinas & Chandraharan, 2016 ; Ghi et al, 2016 ; Cömert & Kocamaz, 2017b ; Yılmaz, 2016 ; Chinnasamy, Muthusamy & Gopal, 2013 ; Sundar, Chitradevi & Geetharamani, 2014 ; Nunes & Ayres-de Campos, 2016 ).…”

Section: Methodsmentioning

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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“…The generalized inverse matrix of H is calculated according to Moore-Penrose. ELM can overcome slow training speed and being stuck in a local minimum of traditional training algorithm and have better generalization and is extensively used in regression and classification problems [18].…”

Section: Data Collectionmentioning

confidence: 99%

“…As mentioned earlier, ELM can overcome limitations of the traditional learning algorithms with better generalization performance, low computational process, and especially extremely fast learning ability [17,18]. However, the ELM structure has some drawbacks.…”

Section: F-scorementioning

confidence: 99%

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Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Cömert

Kocamaz

2017

Acta Phys. Pol. A

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Cardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be utilized in classifying fetal heart rate signals. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. The main aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate signals. Therefore, the research has been focused on the widely used and practical machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative way, fetal heart rate signals were classified as normal or hypoxic using the aforementioned machine learning techniques. The performance metrics derived from confusion matrix were used to measure classifiers' success. According to experimental results, although all machine learning techniques produced satisfactory results, artificial neural network yielded the rather well results with the sensitivity of 99.73% and specificity of 97.94%. The study results show that the artificial neural network was superior to other algorithms.

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Cardiotocography signals with artificial neural network and extreme learning machine

Cited by 37 publications

References 11 publications

Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community

Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community

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

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

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