Arif, M. (2015). 'Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal'. Biomaterials and Biomedical Engineering, 2(3), 173-183. Retrieved from www.scopus.com
Bhatia, M., Mahtani, K. R., Nunan, D., & Reddy, A. (2017). A cross-sectional comparison of three guidelines for intrapartum cardiotocography. International Journal of Gynecology and Obstetrics, 138(1), 89-93. doi:10.1002/ijgo.12161
Chamidah, N., & Wasito, I. (2016). Fetal state classification from cardiotocography based on feature extraction using hybrid K-means and support vector machine. Paper presented at the ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings, 37-41. doi:10.1109/ICACSIS.2015.7415166 Retrieved from www.scopus.com
Chen, C. -., Yu, C., Chang, C. -., & Lin, C. -. (2014). Comparison of a novel computerized analysis program and visual interpretation of cardiotocography. PLoS ONE, 9(12) doi:10.1371/journal.pone.0112296
Chinnasamy, S., Muthusamy, C., & Gopal, G. (2013). An outlier based bi-level neural network classification system for improved classification of cardiotocogram data. Life Science Journal, 10(1), 244-251. Retrieved from www.scopus.com
Chudacek, V., Anden, J., Mallat, S., Abry, P., & Doret, M. (2014). Scattering transform for intrapartum fetal heart rate variability fractal analysis: A case-control study. IEEE Transactions on Biomedical Engineering, 61(4), 1100-1108. doi:10.1109/TBME.2013.2294324
Cömert, Z., & Kocamaz, A. F. (2017). A novel software for comprehensive analysis of cardiotocography signals "CTG-OAS". Paper presented at the IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, doi:10.1109/IDAP.2017.8090210 Retrieved from www.scopus.com
Cömert, Z., & Kocamaz, A. F. (2017). Comparison of machine learning techniques for fetal heart rate classification. Acta Physica Polonica A, 132(3), 451-454. doi:10.12693/APhysPolA.132.451
Comert, Z., Kocamaz, A. F., & Gungor, S. (2016). Classification and comparison of cardiotocography signals with artificial neural network and extreme learning machine. Paper presented at the 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, 1493-1496. doi:10.1109/SIU.2016.7496034 Retrieved from www.scopus.com
Czabański, R., Jezewski, J., Horoba, K., & Jezewski, M. (2013). Fetal state assessment using fuzzy analysis of fetal heart rate signals - agreement with the neonatal outcome. Biocybernetics and Biomedical Engineering, 33(3), 145-155. doi:10.1016/j.bbe.2013.07.003
Czabanski, R., Wrobel, J., Jezewski, J., Leski, J., & Jezewski, M. (2015). Efficient evaluation of fetal wellbeing during pregnancy using methods based on statistical learning principles. J.Med.Imaging Health Inform, 5(6), 1327-1336. Retrieved from www.scopus.com
Das, S., Roy, K., & Saha, C. K. (2016). A novel step towards machine diagnosis of fetal status in-utero: Calculation of baseline variability. Paper presented at the Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2015, 230-234. doi:10.1109/ICRCICN.2015.7434241 Retrieved from www.scopus.com
Das, S., Roy, K., & Saha, C. K. (2015). Determination of window size for baseline estimation of fetal heart rate using CTG. Paper presented at the Proceedings of the 2015 3rd International Conference on Computer, Communication, Control and Information Technology, C3IT 2015, doi:10.1109/C3IT.2015.7060179 Retrieved from www.scopus.com
Di Tommaso, M., Seravalli, V., Cordisco, A., Consorti, G., Mecacci, F., & Rizzello, F. (2013). Comparison of five classification systems for interpreting electronic fetal monitoring in predicting neonatal status at birth. Journal of Maternal-Fetal and Neonatal Medicine, 26(5), 487-490. doi:10.3109/14767058.2012.735726
Fergus, P., Selvaraj, M., & Chalmers, C. (2018). Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using cardiotocography traces. Computers in Biology and Medicine, 93, 7-16. doi:10.1016/j.compbiomed.2017.12.002
Frigo, G., & Giorgi, G. (2017). Comparative evaluation of on-line missing data regression techniques in intrapartum FHR measurements. Paper presented at the I2MTC 2017 - 2017 IEEE International Instrumentation and Measurement Technology Conference, Proceedings, doi:10.1109/I2MTC.2017.7969772 Retrieved from www.scopus.com
Garabedian, C., Butruille, L., Drumez, E., Servan Schreiber, E., Bartolo, S., Bleu, G., . . . Houfflin-Debarge, V. (2017). Inter-observer reliability of 4 fetal heart rate classifications. Journal of Gynecology Obstetrics and Human Reproduction, 46(2), 131-135. doi:10.1016/j.jogoh.2016.11.002
Gavrilis, D., Nikolakopoulos, G., & Georgoulas, G. (2015). A one-class approach to cardiotocogram assessment. Paper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, , 2015-November 518-521. doi:10.1109/EMBC.2015.7318413 Retrieved from www.scopus.com
Georgieva, A., Payne, S. J., Moulden, M., & Redman, C. W. G. (2013). Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications, 22(1), 85-93. doi:10.1007/s00521-011-0743-y
Georgoulas, G., Karvelis, P., Gavrilis, D., Stylios, C. D., & Nikolakopoulos, G. (2017). An ordinal classification approach for CTG categorization. Paper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2642-2645. doi:10.1109/EMBC.2017.8037400 Retrieved from www.scopus.com
Georgoulas, G., Spilka, J., Karvelis, P., Chudáček, V., Stylios, C., & Lhotská, L. (2014). A three class treatment of the FHR classification problem using latent class analysis labeling. Paper presented at the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 46-49. doi:10.1109/EMBC.2014.6943525 Retrieved from www.scopus.com
Ghi, T., Morganelli, G., Bellussi, F., Rucci, P., Giorgetta, F., Rizzo, N., . . . Pilu, G. (2016). Cardiotocographic findings in the second stage of labor among fetuses delivered with acidemia: A comparison of two classification systems. European Journal of Obstetrics and Gynecology and Reproductive Biology, 203, 297-302. doi:10.1016/j.ejogrb.2016.06.028
Hannah Inbarani, H., Nizar Banu, P. K., & Azar, A. T. (2014). Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Computing and Applications, 25(3-4), 793-806. doi:10.1007/s00521-014-1552-x
Haweel, T. I., & Bangash, J. I. (2013). Volterra neural analysis of fetal cardiotocographic signals. Paper presented at the 2013 1st International Conference on Communications, Signal Processing and their Applications, ICCSPA 2013, doi:10.1109/ICCSPA.2013.6487321 Retrieved from www.scopus.com
Jyothi, R., Hiwale, S., & Bhat, P. V. (2017). Classification of labour contractions using KNN classifier. Paper presented at the 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016, 110-115. doi:10.1109/ICSMB.2016.7915100 Retrieved from www.scopus.com
Kim, S. -., Yang, H. -., & Lee, S. -. (2017). FitMine: Automatic mining for time-evolving signals of cardiotocography monitoring. Data Mining and Knowledge Discovery, 31(4), 909-933. doi:10.1007/s10618-017-0493-2
Magenes, G., Bellazzi, R., Malovini, A., & Signorini, M. G. (2016). Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses. Paper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, , 2016-October 916-919. doi:10.1109/EMBC.2016.7590850 Retrieved from www.scopus.com
Martí Gamboa, S., Giménez, O. R., Mancho, J. P., Moros, M. L., Sada, J. R., & Mateo, S. C. (2017). Diagnostic accuracy of the FIGO and the 5-tier fetal heart rate classification systems in the detection of neonatal acidemia. American Journal of Perinatology, 34(5), 508-514. doi:10.1055/s-0036-1593810
Nagendra, V., Gude, H., Sampath, D., Corns, S., & Long, S. (2017). Evaluation of support vector machines and random forest classifiers in a real-time fetal monitoring system based on cardiotocography data. Paper presented at the 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017, doi:10.1109/CIBCB.2017.8058546 Retrieved from www.scopus.com
Nunes, I., & Ayres-De-Campos, D. (2016). Computer analysis of foetal monitoring signals. Best Practice and Research: Clinical Obstetrics and Gynaecology, 30, 68-78. doi:10.1016/j.bpobgyn.2015.02.009
Ocak, H. (2013). A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. Journal of Medical Systems, 37(2) doi:10.1007/s10916-012-9913-4
Ocak, H., & Ertunc, H. M. (2013). Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Computing and Applications, 23(6), 1583-1589. doi:10.1007/s00521-012-1110-3
Pasarica, A., Nemescu, D., Costin, H., & Rotariu, C. (2017). Automatic analysis of cardiotoco-graphic recordings for fetal acidosis study. Medical-Surgical Journal-Revista Medico-Chirurgicala, 121(1), 206-214. Retrieved from www.scopus.com
Permanasari, A. E., & Nurlayli, A. (2018). Decision tree to analyze the cardiotocogram data for fetal distress determination. Paper presented at the Proceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017, , 2018-January 459-463. doi:10.1109/SIET.2017.8304182 Retrieved from www.scopus.com
Pinas, A., & Chandraharan, E. (2016). Continuous cardiotocography during labour: Analysis, classification and management. Best Practice and Research: Clinical Obstetrics and Gynaecology, 30, 33-47. doi:10.1016/j.bpobgyn.2015.03.022
Rei, M., Tavares, S., Pinto, P., Machado, A. P., Monteiro, S., Costa, A., . . . Ayres-De-Campos, D. (2016). Interobserver agreement in CTG interpretation using the 2015 FIGO guidelines for intrapartum fetal monitoring. European Journal of Obstetrics and Gynecology and Reproductive Biology, 205, 27-31. doi:10.1016/j.ejogrb.2016.08.017
Romano, M., Bifulco, P., Ponsiglione, A. M., Gargiulo, G. D., Amato, F., & Cesarelli, M. (2018). Evaluation of floatingline and foetal heart rate variability. Biomedical Signal Processing and Control, 39, 185-196. doi:10.1016/j.bspc.2017.07.018
Romano, M., Bifulco, P., Ruffo, M., Improta, G., Clemente, F., & Cesarelli, M. (2016). Software for computerised analysis of cardiotocographic traces. Computer Methods and Programs in Biomedicine, 124, 121-137. doi:10.1016/j.cmpb.2015.10.008
Rotariu, C., Pasarica, A., Andruseac, G., Costin, H., & Nemescu, D. (2014). Automatic analysis of the fetal heart rate variability and uterine contractions. Paper presented at the EPE 2014 - Proceedings of the 2014 International Conference and Exposition on Electrical and Power Engineering, 553-556. doi:10.1109/ICEPE.2014.6969970 Retrieved from www.scopus.com
Sahin, H., & Subasi, A. (2015). Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Applied Soft Computing Journal, 33, 231-238. doi:10.1016/j.asoc.2015.04.038
Shah, S. A. A., Aziz, W., Arif, M., & Nadeem, M. S. A. (2016). Decision trees based classification of cardiotocograms using bagging approach. Paper presented at the Proceedings - 2015 13th International Conference on Frontiers of Information Technology, FIT 2015, 12-17. doi:10.1109/FIT.2015.14 Retrieved from www.scopus.com
Spilka, J., Frecon, J., Leonarduzzi, R., Pustelnik, N., Abry, P., & Doret, M. (2017). Sparse support vector machine for intrapartum fetal heart rate classification. IEEE Journal of Biomedical and Health Informatics, 21(3), 664-671. doi:10.1109/JBHI.2016.2546312
Sundar, C., Chitradevi, M., & Geetharamani, G. (2014). Incapable of identifying suspicious records in CTG data using ANN based machine learning techniques. Journal of Scientific and Industrial Research, 73(8), 510-516. Retrieved from www.scopus.com
Tomas, P., Krohova, J., Dohnalek, P., & Gajdos, P. (2013). Classification of cardiotocography records by random forest. Paper presented at the 2013 36th International Conference on Telecommunications and Signal Processing, TSP 2013, 620-623. doi:10.1109/TSP.2013.6614010 Retrieved from www.scopus.com
Warmerdam, G. J. J., Vullings, R., Van Laar, J. O. E. H., Hout-Van Der Jagt, M. B. V. D., Bergmans, J. W. M., Schmitt, L., & Oei, S. G. (2016). Selective heart rate variability analysis to account for uterine activity during labor and improve classification of fetal distress. Paper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, , 2016-October 2950-2953. doi:10.1109/EMBC.2016.7591348 Retrieved from www.scopus.com
Warmerdam, G. J. J., Vullings, R., Van Laar, J. O. E. H., Van Der Hout-Van Der Jagt,M.B., Bergmans, J. W. M., Schmitt, L., & Oei, S. G. (2018). Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis. Physiological Measurement, 39(2) doi:10.1088/1361-6579/aaa925
Wróbel, J., Horoba, K., Pander, T., Jezewski, J., & Czabański, R. (2013). Improving fetal heart rate signal interpretation by application of myriad filtering. Biocybernetics and Biomedical Engineering, 33(4), 211-221. doi:10.1016/j.bbe.2013.09.004
Xu, L., Redman, C. W. G., Payne, S. J., & Georgieva, A. (2014). Feature selection using genetic algorithms for fetal heart rate analysis. Physiological Measurement, 35(7), 1357-1371. doi:10.1088/0967-3334/35/7/1357
Yılmaz, E. (2016). Fetal state assessment from cardiotocogram data using artificial neural networks. Journal of Medical and Biological Engineering, 36(6), 820-832. doi:10.1007/s40846-016-0191-3
Zhang, Y., & Zhao, Z. (2018). Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost. Paper presented at the Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, , 2018-January 1-6. doi:10.1109/CISP-BMEI.2017.8302314 Retrieved from www.scopus.com
Zhang, Y., & Zhao, Z. (2018). Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost. Paper presented at the Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, , 2018-January 1-6. doi:10.1109/CISP-BMEI.2017.8302314 Retrieved from www.scopus.com