{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T06:05:48Z","timestamp":1792821948641,"version":"4.5.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19K22602"],"award-info":[{"award-number":["19K22602"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["18H05525"],"award-info":[{"award-number":["18H05525"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJER1801"],"award-info":[{"award-number":["JPMJER1801"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH\u2009&lt;\u20097.20 or Apgar score at 1\u00a0min\u2009&lt;\u20097) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73\u2009\u00b1\u20090.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.<\/jats:p>","DOI":"10.1038\/s41598-021-92805-9","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T06:03:17Z","timestamp":1624860197000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Deep neural network-based classification of cardiotocograms outperformed conventional algorithms"],"prefix":"10.1038","volume":"11","author":[{"given":"Jun","family":"Ogasawara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Ikenoue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroko","family":"Yamamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Motoshige","family":"Sato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshifumi","family":"Kasuga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasue","family":"Mitsukura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuji","family":"Ikegaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masato","family":"Yasui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamoru","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daigo","family":"Ochiai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"92805_CR1","doi-asserted-by":"crossref","unstructured":"Ayres-de-Campos, D., Spong, C. Y. & Chandraharan, E. for FIGO Intrapartum Fetal Monitoring Expert Consensus Panel. FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. Int J Gynaecol Obstet. 131, 13-24. (2015).","DOI":"10.1016\/j.ijgo.2015.06.020"},{"issue":"5","key":"92805_CR2","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1097\/AOG.0b013e3182004fa9","volume":"116","author":"American College of Obstetricians and Gynecologists","year":"2010","unstructured":"American College of Obstetricians and Gynecologists. Practice bulletin no. 116: Management of intrapartum fetal heart rate tracings. Obstet. Gynecol. 116(5), 1232\u20131240 (2010).","journal-title":"Obstet. Gynecol."},{"key":"92805_CR3","doi-asserted-by":"crossref","unstructured":"Alfirevic, A., Devane, D., Gyte, G. M. L. Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst Rev. 3, CD006066. (2017)","DOI":"10.1002\/14651858.CD006066.pub3"},{"key":"92805_CR4","first-page":"298","volume":"34","author":"P Outi","year":"2006","unstructured":"Outi, P., Tiina, L., Riikka, L. & Risto, T. Intrapartum cardiotocography\u2014The dilemma of interpretational variation. J. Perinat. Med. 34, 298\u2013302 (2006).","journal-title":"J. Perinat. Med."},{"key":"92805_CR5","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/S0020-7292(97)02846-4","volume":"57","author":"J Bernardes","year":"1997","unstructured":"Bernardes, J., Costa-Pereira, A., Ayres-de-Campos, D. & Pereira-Leite, L. Evaluation of interobserver agreement of cardiotocograms. Int. J. Gynecol. 57, 33\u201337 (1997).","journal-title":"Int. J. Gynecol."},{"issue":"3","key":"92805_CR6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1159\/000327133","volume":"72","author":"S Schiermeier","year":"2011","unstructured":"Schiermeier, S., Westhof, G., Leven, A., Hatzmann, H. & Reinhard, J. Intra- and interobserver variability of intrapartum cardiotocography: A multicenter study comparing the FIGO classification with computer analysis software. Gynecol. Obstet. Investig. 72(3), 169\u2013173 (2011).","journal-title":"Gynecol. Obstet. Investig."},{"key":"92805_CR7","unstructured":"Japan Council for Quality Health Care, Cardiotocograms of Cerebral Palsy Cases, 8th annual report of Japan Society of Perinatal and Neonatal Medicine (2018)."},{"key":"92805_CR8","doi-asserted-by":"publisher","first-page":"73085","DOI":"10.1109\/ACCESS.2018.2877933","volume":"7","author":"JAL Marques","year":"2019","unstructured":"Marques, J. A. L., Cortez, P., Madeiro, J. & Fong, S. Automatic cardiotocography diagnostic system based on Hilbert transform and adaptive threshold technique. IEEE Access 7, 73085\u201373094 (2019).","journal-title":"IEEE Access"},{"key":"92805_CR9","doi-asserted-by":"crossref","unstructured":"Warrick, P. A. & Hamilton, E. F. LSTM modeling of perinatal fetal heart rate. Computing in Cardiology (CinC). 1\u20134 (2019).","DOI":"10.22489\/CinC.2019.419"},{"key":"92805_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, Y. & Zhao, Z. Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost. BioMedical Engineering and Informatics, 1\u20136 (2017).","DOI":"10.1109\/CISP-BMEI.2017.8302314"},{"issue":"23","key":"92805_CR11","doi-asserted-by":"publisher","first-page":"8619","DOI":"10.1073\/pnas.1403112111","volume":"111","author":"DLK Yamins","year":"2014","unstructured":"Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111(23), 8619\u20138624 (2014).","journal-title":"PNAS"},{"key":"92805_CR12","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342\u20131350 (2018).","journal-title":"Nat. Med."},{"key":"92805_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"key":"92805_CR14","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1471-2393-14-16","volume":"14","author":"V Chud\u00e1\u010dek","year":"2014","unstructured":"Chud\u00e1\u010dek, V. et al. Open access intrapartum CTG database. BMC Pregnancy Childbirth 14, 16 (2014).","journal-title":"BMC Pregnancy Childbirth"},{"issue":"56","key":"92805_CR15","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014).","journal-title":"J. Mach. Learn. Res."},{"key":"92805_CR16","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.ajog.2003.07.014","volume":"190","author":"H Sameshima","year":"2004","unstructured":"Sameshima, H., Ikenoue, T., Ikeda, T., Kamitomo, M. & Ibara, S. Unselected low-risk pregnancies and the effect of continuous intrapartum fetal heart rate monitoring on umbilical blood gases and cerebral palsy. Am. J. Obstet. Gynecol. 190, 118\u2013123 (2004).","journal-title":"Am. J. Obstet. Gynecol."},{"key":"92805_CR17","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1186\/s12911-019-1007-5","volume":"19","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z. et al. DeepFHR: Intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. BMC Med. Inform. Decis. Making 19, 286 (2019).","journal-title":"BMC Med. Inform. Decis. Making"},{"key":"92805_CR18","doi-asserted-by":"crossref","unstructured":"Petrozziello, A., Jordanov, I., Papageorghiou, T. A., Redman, W. G. C. & Georgieva A. Deep learning for continuous electronic fetal monitoring in labor. Annu Int Conf IEEE Eng. Med. Biol. Soc., 5866\u20135869 (2018).","DOI":"10.1109\/EMBC.2018.8513625"},{"issue":"5","key":"92805_CR19","doi-asserted-by":"publisher","first-page":"5231","DOI":"10.1016\/j.ajog.2018.01.026","volume":"218","author":"AG Cahill","year":"2018","unstructured":"Cahill, A. G. et al. A prospective cohort study of fetal heart rate monitoring: Deceleration area is predictive of fetal acidemia. Am. J. Obstet. Gynecol. 218(5), 5231\u201352312 (2018).","journal-title":"Am. J. Obstet. Gynecol."},{"issue":"21","key":"92805_CR20","doi-asserted-by":"publisher","first-page":"2578","DOI":"10.1080\/14767058.2016.1256993","volume":"30","author":"SM Gamboa","year":"2017","unstructured":"Gamboa, S. M., Moros, M. L., Mancho, J. P., Moros, C. L. & Mateo, S. C. Deceleration area and fetal acidemia. J. Matern. Fetal Neonatal Med. 30(21), 2578\u20132584 (2017).","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"92805_CR21","unstructured":"Howard, A. G., Zhu, M., Adam, H. et al. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)."},{"key":"92805_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Rabinovich, A. et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1\u20139 (2015).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"92805_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"92805_CR24","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1111\/j.1651-2227.2006.00178.x","volume":"96","author":"AK Akobeng","year":"2007","unstructured":"Akobeng, A. K. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 96(5), 644\u2013647 (2007).","journal-title":"Acta Paediatr."},{"key":"92805_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0210103","volume":"14","author":"S Goto","year":"2019","unstructured":"Goto, S. et al. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. PLoS\u00a0One 14, e0210103 (2019).","journal-title":"PLoS\u00a0One"},{"key":"92805_CR26","doi-asserted-by":"crossref","unstructured":"Lawhern, V. J, Solon, A. J., Lance, B. J. et al. EEGNet: A compact convolutional neural network for EEG-based brain\u2013computer interfaces. J. Neural Eng. 15, 056013 (2018).","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"92805_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101684","volume":"22","author":"A Emami","year":"2019","unstructured":"Emami, A. et al. Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. Neuroimage Clin. 22, 101684 (2019).","journal-title":"Neuroimage Clin."},{"key":"92805_CR28","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995).","journal-title":"Mach. Learn."},{"key":"92805_CR29","unstructured":"MacQueen, J. B. Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 281\u2013297, (1967)."},{"issue":"1","key":"92805_CR30","first-page":"101","volume":"18","author":"AAF Aziz","year":"2017","unstructured":"Aziz, A. A. F., Shapiai, I. M., Setiawan, A. N. & Mitsukura, Y. Classification of human concentration in EEG signals using Hilbert Huang transform. Int. J. Simul. Syst. Sci. Technol. 18(1), 101\u20131011 (2017).","journal-title":"Int. J. Simul. Syst. Sci. Technol."},{"key":"92805_CR31","unstructured":"Abadi, M., Barham, P., Zheng, X. et al. Tensorflow: A system for large-scale machine learning. 12th USENIX Conference on Operating Systems Design and Implementation, 265\u2013283 (2016)."},{"key":"92805_CR32","unstructured":"Chollet, F., Keras (2015). https:\/\/github.com\/fchollet\/keras."},{"key":"92805_CR33","unstructured":"Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)."},{"key":"92805_CR34","unstructured":"Clevert, DA., Unterthiner, T. & Hochreiter, S. Fast and accurate deep network learning by exponential linear units (ELUs) . arXiv:1511.07289(2015)."},{"key":"92805_CR35","first-page":"1097","volume":"2012","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 2012, 1097\u20131105 (2012).","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"92805_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101669","volume":"55","author":"AK Mukhopadhyay","year":"2020","unstructured":"Mukhopadhyay, A. K. & Samui, S. An experimental study on upper limb position invariant EMG signal classification based on deep neural network. Biomed. Signal Process Control 55, 101669 (2020).","journal-title":"Biomed. Signal Process Control"},{"key":"92805_CR37","unstructured":"Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv:1412.6980 (2015)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-92805-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-92805-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-92805-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T05:17:40Z","timestamp":1670044660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-92805-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["92805"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-92805-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,28]]},"assertion":[{"value":"13 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"13367"}}