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

Cömert, Zafer; Kocamaz, Adnan Fatih

doi:10.5120/ijca2016912417

Cited by 21 publications

(15 citation statements)

References 22 publications

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“…Fanelli et al [15] introduced a new nonlinear parameter based on the phase-rectified signal average (PRSA) for the quantitative assessment of fetal well-being and achieved an area under the curve (AUC) of 75% using the univariate analysis method. Comert et al [16] applied an artificial neural network (ANN) and performed a classification with an Acc, sensitivity (Se), and specificity (Sp) of 92.40, 95.89 and 74.75%, respectively. Obviously, the feature engineering has dominated over conventional methods involving the difficult process of informative feature extraction and optimal feature selection, which is time-consuming, and may result in loss of physiological information regarding the fetus during the overall procedure.…”

Section: Introductionmentioning

confidence: 99%

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

Zhao

Deng

Zhang

et al. 2019

BMC Med Inform Decis Mak

100

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BackgroundFetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions.MethodsIn this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML.ResultsBased on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectivelyConclusionsOnce the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

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Section: Introductionmentioning

confidence: 99%

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

Zhao

Deng

Zhang

et al. 2019

BMC Med Inform Decis Mak

100

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“…The majority of these studies focused on either the detection of basic features reflecting FHR characteristics ( Dawes et al, 1982 ; Mantel et al, 1990 ; Cesarelli et al, 2009 ) or emulating what experts do in their visual examination ( Dawes et al, 1991 ; Keith and Greene, 1994 ). Recently proposed systems have been equipped with advanced signal processing, pattern recognition, and machine learning (ML) techniques to anticipate adverse outcomes ( Krupa et al, 2011 ; Czabanski et al, 2012 ; Spilka et al, 2012 , 2014 , 2017 ; Fanelli et al, 2013 ; Dash et al, 2014 ; Xu et al, 2014 ; Doret et al, 2015 ; Comert and Kocamaz, 2016 ; Georgoulas et al, 2017 ; Comert et al, 2018 ). This approach has three key stages: preprocessing, feature transformation (feature extraction and selection), and classification, which can be briefly described as follows.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the linear and statistical features coming from the time-domain and frequency-domain are extracted to support the automated analysis ( Czabanski et al, 2012 ; Dash et al, 2014 ; Spilka et al, 2014 ). Further, using non-linear parameters (e.g., entropy, complexity, and fractal dimension) in fetal state assessment have been proposed and tested ( Spilka et al, 2012 ; Fanelli et al, 2013 ; Doret et al, 2015 ; Comert and Kocamaz, 2016 ). Recently, an image-based time-frequency (IBTF) feature analysis approach comprised of a combination of short term Fourier transform (STFT) and a gray level co-occurrence matrix (GLCM) have been employed as diagnostic indices for fetal hypoxia detection ( Comert et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Czabanski et al (2012) designed an expert system to predict neonatal pathology using a two-stage analysis method based on weighted fuzzy scoring (WFS) and least square support machine (LS-SVM) and obtained good performance with an accuracy of 92.0%. Comert and Kocamaz (2016) applied an artificial neural network (ANN) and performed a classification task with an accuracy, sensitivity, and specificity of 92.40, 95.89, and 74.75%, respectively.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Computer-based applications in biomedical signal processing are based on several fundamental steps such as preprocessing, feature transform covering extraction and selection of features, and classification (Cömert and Kocamaz 2016). When looking over previously reported studies, it can be seen that there are many feature extraction methods is used so as to describe ECG signals.…”

Section: Introductionmentioning

confidence: 99%

A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals

Di̇ker

Cömert

Avcı³

2017

Bitlis Eren University Journal of Science and Technology

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

Cited by 21 publications

References 22 publications

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

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

A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals

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