Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks

Cardiotocography is the most widely used method in obstetrics practice for monitoring fetal health status. The main goal of monitoring is early detection of fetal hypoxia. A cardiotocogram is a recording of fetal heart rate and uterine activity signals. The accurate analysis of cardiotocograms is critical for further treatment. Therefore, fetal state assessment using machine learning methods from cardiotocogram data has received significant attention in the literature. In this paper, a comparative study of fetal state assessment is presented by using three artificial neural network models, namely the multilayer perceptron neural network, probabilistic neural network, and generalized regression neural network. The performances of the models are evaluated using publicly available cardiotocogram data by running a tenfold cross-validation procedure. The models’ performances are compared in terms of overall classification accuracy. For further analysis, receiver operation characteristic analysis and the cobweb representation technique are used.

[1]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[2]  Banu Diri,et al.  Visualization and analysis of classifiers performance in multi-class medical data , 2008, Expert Syst. Appl..

[3]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[4]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[5]  Elif Derya íbeyli.  Combined neural networks for diagnosis of erythemato-squamous diseases , 2009 .

[6]  Yong Cai,et al.  Benefits and Challenges of Electronic Health Record System on Stakeholders: A Qualitative Study of Outpatient Physicians , 2012, Journal of Medical Systems.

[7]  M. Hariharan,et al.  A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being , 2015, Comput. Math. Methods Medicine.

[8]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[9]  Z. Alfirevic,et al.  Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. , 2017, The Cochrane database of systematic reviews.

[10]  J. Łęski,et al.  The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy , 2014 .

[11]  M. Avci,et al.  Classification of escherichia coli bacteria by artificial neural networks , 2002, Proceedings First International IEEE Symposium Intelligent Systems.

[12]  Anthony T. C. Goh,et al.  Probabilistic neural network for evaluating seismic liquefaction potential , 2002 .

[13]  Jacques de Villiers,et al.  Backpropagation neural nets with one and two hidden layers , 1993, IEEE Trans. Neural Networks.

[14]  M. Chitradevi,et al.  An Overview of Research Challenges for Classification of cardiotocogram Data , 2013, J. Comput. Sci..

[15]  Hasan Ocak,et al.  A Medical Decision Support System Based on Support Vector Machines and the Genetic Algorithm for the Evaluation of Fetal Well-Being , 2013, Journal of Medical Systems.

[16]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[17]  H. White,et al.  Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.

[18]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[19]  Guang-Bin Huang,et al.  Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.

[20]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[21]  J A Swets,et al.  Better decisions through science. , 2000, Scientific American.

[22]  Ron Kohavi,et al.  Guest Editors' Introduction: On Applied Research in Machine Learning , 1998, Machine Learning.

[23]  M. Chitradevi,et al.  Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique , 2012 .

[24]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[25]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[26]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[27]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[28]  Robert P. W. Duin,et al.  Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Rodolfo Zunino,et al.  Implementing probabilistic Neural Networks , 1997, Neural Computing & Applications.

[30]  Takéhiko Nakama,et al.  Comparisons of Single- and Multiple-Hidden-Layer Neural Networks , 2011, ISNN.

[31]  Wee Ser,et al.  Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[32]  Ersen Yilmaz,et al.  Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree , 2013, Comput. Math. Methods Medicine.

[33]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[34]  Yue Chen,et al.  Research on EEG Classification with Neural Networks Based on the Levenberg-Marquardt Algorithm , 2012, ICICA.

[35]  Mia K. Markey,et al.  Comparison of three-class classification performance metrics: a case study in breast cancer CAD , 2005, SPIE Medical Imaging.

[36]  Hasan Ocak,et al.  Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems , 2012, Neural Computing and Applications.

[37]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[38]  José Hernández-Orallo,et al.  Volume under the ROC Surface for Multi-class Problems , 2003, ECML.

[39]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[40]  Alina A. von Davier,et al.  Cross-Validation , 2014 .

[41]  Shuxiang Xu,et al.  A novel approach for determining the optimal number of hidden layer neurons for FNN’s and its application in data mining , 2008 .

[42]  Chrysostomos D. Stylios,et al.  Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines , 2006, IEEE Transactions on Biomedical Engineering.

[43]  Mei-Ling Huang,et al.  Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network , 2012 .

[44]  D. Ayres-de- Campos,et al.  SisPorto 2.0: a program for automated analysis of cardiotocograms. , 2000, The Journal of maternal-fetal medicine.

[45]  Shin'ichi Tamura,et al.  Capabilities of a four-layered feedforward neural network: four layers versus three , 1997, IEEE Trans. Neural Networks.

[46]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[47]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[48]  Seongjin Choi,et al.  Prediction of plasma etching using a randomized generalized regression neural network , 2004 .

[49]  András Kocsor,et al.  ROC analysis: applications to the classification of biological sequences and 3D structures , 2008, Briefings Bioinform..

[50]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[51]  Turgay Ibrikci,et al.  Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach , 2014 .