Abstract
Cardiotocogram (CTG), one of the methods for the overall evaluation of fetus condition. It is entails two significant recordings, the Contractions in Uterus (UC) and Heart Rate of Fetus (FHR). Multiple methods are already used to interpret the CTG. Maximum use either a binary classification method or a multiclass method to make a judgement about the class with which the finding belongs to! This current work takes two datasets available today that contains different features. Dataset-1 and 2 contain the recording of fetus movement. Dataset-1 has observations that are taken internally by placing electrode on the scalp of fetus. Other maternal factors are also mentioned. Dataset-2 contains the CTG readings taken externally by placing probe on the abdomen on the pregnant ladies. Sometimes the readings taken externally might not be much accurate because of the very placement of probe and mother’s physical state. And readings those are recorded internally may cause infection to mother and fetus. The use of three-class (1, 2 or 3) approach to the evaluate cardiotocograms and proposing a model for the healthy data. The results indicate that the features of dataset-1 are more significant for predicting the state of fetus.
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References
Sampath V (2018) Computational intelligence methods for predicting fetal outcomes from heart rate patterns. Theses, Spring
Alnuaimi S, Jimaa S, Khandoker A (2017) Fetal cardiac doppler signal processing techniques: challenges and future research directions. Front Bioeng Biotechnol 5(82):1–8
Comert Z, Kocamaz A (2017) A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”. IEEE
Saleem S, Naqvi SS, Manzoor T, Saeed A, Rehman N, Mirza J (2019) A strategy for classification of “vaginal vs. cesarean section” delivery: bivariate empirical mode decomposition of cardiotocographic recordings. Front Physiol 246(10):1–18
Rishika Reddy A, Suresh Kumar P (2016) Predictive big data analytics in healthcare. In: 2016 second international conference on computational intelligence and communication technology, IEEE. https://doi.org/10.1109/CICT.2016.129
Mishra S, Sahoo S, Mishra BK (2019) Addressing security issues and standards in Internet of things. In: Emerging trends and applications in cognitive computing. IGI Global, pp 224–257
Hoodbhoy Z, Noman M, Shafique A, Nasim A, Chowdhury D, Hasan B (2019) Use of machine learning algorithm for prediction of fetal risk using cardiotocographic data. Int J Appl Basic Med Res 9(4):226–230
Xu L, Georgieva A, Redman C, Payne S (2013) Feature selection for computerized fetal heart analysis using genetic algorithm. In: 35th annual international IEEE conference, July 3–7, pp 445–448
Zhang Y, Zhao Z (2017) Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost. In: 10th international congress on image and signal processing, biomedical engineering and informatics, IEEE
Chen C, Chen J, Yu C, Lin C (2009) A comparative study of a new cardiotocography analysis program. In: 31st annual international conference of the IEEE EMBS, Sep. 2-6, pp 2567–2570
Zakaria N, Numan P, Malarvili MB (2018) Fetal movements recording system using accelerometer sensor. ARPN J Eng Appl Sci 13(3):1022–1032
Zhao Z, Zhang Y, Deng Y (2018) Article, A comprehensive feature analysis of the fetal heart rate signal for the intelligent assessment of fetal state. J Clinical Med 7:223. https://doi.org/10.3390/jcm7080223
Yilmaz E, Kilikcier C (2013) Research article, “determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Hindawi Publication Corporation, vol. 487179, pp 1–8
Zhao Z, Deng Y, Zhang Y, Zhang Y, Zhang X, Shao L (2019) Deep FHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network
Arif M (2015) Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal. Biomater Biomech Bioeng 2(3):173–183
Kaur H, Khullar V, Singh H, Bala M (2019) Perinatal hypoxia diagnostic system by using scalable machine learning algorithms. Int J Innov Technol Explor Eng 8(12):1954–1959. ISSN: 2278–3075
Vovsha I, Rajan A, Salleb-Aouissi A, Raja A, Radeva A, DIab H, Tomar A, Wapner R (2021) Predicting preterm birth is not elusive: machine learning paves the way to individual wellness. In: AAAI spring symposium, pp 82–89
Zhao X, Zeng X, Koehl L, Tartare G, Jonckheere J, Song K (2019) An IoT-based wearable system using accelerometers and machine learning for fetal movement monitoring. IEEE 299–304
Spilka J et al (2014) Analysis of obstetricians’ decision making on CTG recordings. J Biomed Inform 51:72–79. https://doi.org/10.1016/j.jbi.2014.04.010
Fuentealba P, Illanes A, Ortmeier F (2017) Analysis of the foetal heart rate in cardiotocographic recordings through a progressive characterization of decelerations. Curr Dir Biomed Eng 3(2):423–427. https://doi.org/10.1515/cdbme-2017-0089
Pilarczyk R, Strózik M, Hirnle L (2020) Diagnostic equivalency of mobile CTG devices and remote analysis to conventional on-site nonstress test. Adv Clin Exp Med 29(1):33–44. https://doi.org/10.17219/acem/111812
Mangesi L, Hofmeyr GJ, Smith V, Smyth RMD (2015) Fetal movement counting for assessment of fetal wellbeing. Cochrane Database Syst Rev 10:2015. https://doi.org/10.1002/14651858.CD004909.pub3
Fanelli A, Magenes G, Campanile M, Signorini MG (2013) Quantitative assessment of fetal well-being through ctg recordings: a new parameter based on phase-rectified signal average. IEEE J Biomed Heal Inform 17(5):959–966. https://doi.org/10.1109/JBHI.2013.2268423
Tan KH, Sabapathy A, Wei X (2013) Fetal manipulation for facilitating tests of fetal wellbeing. Cochrane Database Syst Rev 12:2013. https://doi.org/10.1002/14651858.CD003396.pub2
Tan KH, Smyth RMD, Wei X (2013) Fetal vibroacoustic stimulation for facilitation of tests of fetal wellbeing. Cochrane Database Syst Rev 12:2013. https://doi.org/10.1002/14651858.CD002963.pub2
Georgoulas G, Karvelis P, Spilka J, Chudáček V, Stylios CD, Lhotská L (2017) Investigating pH based evaluation of fetal heart rate (FHR) recordings. Health Technol (Berl) 7(2–3):241–254. https://doi.org/10.1007/s12553-017-0201-7
Ali Kadhim NJ, Kadhim Abed J (2020) Enhancing the prediction accuracy for cardiotocography (CTG) using firefly algorithm and naive bayesian classifier. In: IOP conference series material science engineering, vol 745(1). https://doi.org/10.1088/1757-899X/745/1/012101
Al-yousif S et al (2021) A systematic review of automated preprocessing, feature extraction and classification of cardiotocography. PeerJ Comput Sci 7:1–37. https://doi.org/10.7717/peerj-cs.452
Khandoker A, Ibrahim E, Oshio S, Kimura Y (2018) Validation of beat by beat fetal heart signals acquired from four-channel fetal phonocardiogram with fetal electrocardiogram in healthy late pregnancy. Sci Rep 8(1):1–11. https://doi.org/10.1038/s41598-018-31898-1
Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B (2020) Machine learning in fetal cardiology: what to expect. Fetal Diagn Ther 47(5):363–372. https://doi.org/10.1159/000505021
Alyousif S, Mohd M, Bilal B, Sheikh M, Algunaidi M (2017) Rule-based algorithm for intrapartum cardiotocograph pattern features extraction and classification. Heal. Sci. J. 10(6):1–13. https://doi.org/10.21767/1791-809x.1000468
Romano M, Ponsiglione AM (2014) Computerized cardiotocography: a software to generate synthetic signals. J Heal Med Inform 05(04). https://doi.org/10.4172/2157-7420.1000162
Blix E et al (2019) Intermittent auscultation fetal monitoring during labour: A systematic scoping review to identify methods, effects, and accuracy. PLoS ONE 14(7):1–21. https://doi.org/10.1371/journal.pone.0219573
Chudcek V, Spilka J, Bursa M, Janku P, Hruban L, Huptych M, Lhotska L (2014) Database, “Open access intrapartum CTG database,” BMC Pregnancy and Childbirth, pp 1–12
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Sharma, S., Ashu, Gupta, A., Nayak, S.R. (2022). Quantitative Assessment of Fetal Wellbeing Through CTG Recordings. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_13
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