Efficient approach for digitization of the cardiotocography signals

Abstract Cardiotocography (CTG) is generally provided on printed traces, and digitization of CTG signal is important for forthcoming assessments. In this paper, a new algorithm relies on the box-counting method is offered for the digitization of the CTG signals from CTG printed traces. The introduced algorithm inputs the CTG printed traces and outputs the digital fetal heart rate (FHR) and uterine contraction (UC) signals. The proposed method initially extracts the CTG signal image and gridded background image. Retrieving of the FHR and UC signals on the gridded background disrupts the background grids. So, we employ an algorithm to fix the degraded lines in the gridded background. After the line fixing operation, the boxes in the horizontal and vertical axes are counted for determining the calibration parameters. A set of specific equations are used to determine the calibration parameters. The signal extraction is performed on by red-channel thresholding of input CTG printing images. An open-access CTG intrapartum database comprises 552 samples is used in the experiment. As a result, the average correlation coefficients of FHR and UC signals are 0.9811 ± 0.0251 and 0.9905 ± 0.0126, respectively.

[1]  Laura Burattini,et al.  Digital cardiotocography: What is the optimal sampling frequency? , 2019, Biomed. Signal Process. Control..

[2]  João Bernardes,et al.  SisPorto 4.0 – computer analysis following the 2015 FIGO Guidelines for intrapartum fetal monitoring , 2017, The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians.

[3]  Sandro Fioretti,et al.  eCTG: an automatic procedure to extract digital cardiotocographic signals from digital images , 2018, Comput. Methods Programs Biomed..

[4]  Zafer Cömert,et al.  Open-access software for analysis of fetal heart rate signals , 2018, Biomed. Signal Process. Control..

[5]  Srinivasan Jayaraman,et al.  A Novel Technique for ECG Morphology Interpretation and Arrhythmia Detection Based on Time Series Signal Extracted from Scanned ECG Record , 2012 .

[6]  J. Álvarez-Ramírez,et al.  Fractal and nonlinear changes in the long-term baseline fluctuations of fetal heart rate. , 2012, Medical engineering & physics.

[7]  Edmond Zahedi,et al.  Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine , 2011, Biomedical engineering online.

[8]  Carlo Cattani,et al.  On the Creation of a New Diagnostic Model for Fetal Well-Being on the Base of Wavelet Analysis of Cardiotocograms , 2006, Journal of Medical Systems.

[9]  Sarah Rhöse,et al.  Inter‐ and intra‐observer agreement of non‐reassuring cardiotocography analysis and subsequent clinical management , 2014, Acta obstetricia et gynecologica Scandinavica.

[10]  Francesco Amato,et al.  Evaluation of floatingline and foetal heart rate variability , 2018, Biomed. Signal Process. Control..

[11]  Maria Romano,et al.  Foetal heart rate variability frequency characteristics with respect to uterine contractions , 2010 .

[12]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[13]  A. Porta,et al.  Symbolic Dynamics of Heart Rate Variability: A Probe to Investigate Cardiac Autonomic Modulation , 2005, Circulation.

[14]  Zafer Cömert,et al.  Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community , 2016 .

[15]  Rafael Dueire Lins,et al.  Converting ECG and Other Paper Legated Biomedical Maps into Digital Signals , 2007, GREC.

[16]  Umit Budak,et al.  A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces , 2019, IRBM.

[17]  D. Ayres-de-Campos,et al.  FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography , 2015, International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics.

[18]  Lenka Lhotská,et al.  Using nonlinear features for fetal heart rate classification , 2012, Biomed. Signal Process. Control..

[19]  Jacek M. Leski,et al.  Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment , 2019, Expert Syst. Appl..

[20]  Zafer Cömert,et al.  Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment , 2018, Comput. Biol. Medicine.

[21]  Lenka Lhotská,et al.  Open access intrapartum CTG database , 2014, BMC Pregnancy and Childbirth.

[22]  João Bernardes,et al.  An overview of central fetal monitoring systems in labour , 2013, Journal of perinatal medicine.

[23]  Yang Zhang,et al.  Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network , 2019, Front. Physiol..

[24]  Maria Romano,et al.  Software for computerised analysis of cardiotocographic traces , 2016, Comput. Methods Programs Biomed..

[25]  Maria G. Signorini,et al.  Quantitative Assessment of Fetal Well-Being Through CTG Recordings: A New Parameter Based on Phase-Rectified Signal Average , 2013, IEEE Journal of Biomedical and Health Informatics.

[26]  Dimitrios Kassanos,et al.  Wavelet analysis and neural networks for intrapartum fetal monitoring. Can we long for a new technique? Is it doable? , 2008, Medical science monitor : international medical journal of experimental and clinical research.

[27]  Subha Velappan,et al.  Genetic Algorithm Based Feature Subset Selection for Fetal State Classification , 2015 .

[28]  Yang Zhang,et al.  A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State , 2018, Journal of clinical medicine.

[29]  Luis Filipe Coelho Antunes,et al.  Entropy and Compression Capture Different Complexity Features: The Case of Fetal Heart Rate , 2017, Entropy.