{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:31:45Z","timestamp":1771547505272,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819947416","type":"print"},{"value":"9789819947423","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-4742-3_18","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:02:38Z","timestamp":1690675358000},"page":"222-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MT-1DCG: A Novel Model for Multivariate Time Series Classification"],"prefix":"10.1007","author":[{"given":"Yu","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanwen","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zichang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianghua","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Asfaw, D., Jordanov, I., Impey, L., Namburte, A., Lee, R., Georgieva, A.: Multimodal deep learning for predicting adverse birth outcomes based on early labour data. Bioengineering 10(6), 730 (2023)","DOI":"10.3390\/bioengineering10060730"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Baghel, N., Burget, R., Dutta, M.K.: 1D-FHRNet: automatic diagnosis of fetal acidosis from fetal heart rate signals. Biomed. Signal Process. Control 71, 102794 (2022)","DOI":"10.1016\/j.bspc.2021.102794"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Boudet, S., Houz\u00e9 de l\u2019Aulnoit, A., Peyrodie, L., Demailly, R., Houz\u00e9 de l\u2019Aulnoit, D.: Use of deep learning to detect the maternal heart rate and false signals on fetal heart rate recordings. Biosensors 12(9), 691 (2022)","DOI":"10.3390\/bios12090691"},{"key":"18_CR4","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"18_CR5","unstructured":"Cui, Z., Chen, W., Chen, Y.: Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995 (2016)"},{"issue":"3","key":"18_CR6","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.ajog.2004.05.059","volume":"191","author":"KL Dervaitis","year":"2004","unstructured":"Dervaitis, K.L., Poole, M., Schmidt, G., Penava, D., Natale, R., Gagnon, R.: ST segment analysis of the fetal electrocardiogram plus electronic fetal heart rate monitoring in labor and its relationship to umbilical cord arterial blood gases. Am. J. Obstet. Gynecol. 191(3), 879\u2013884 (2004)","journal-title":"Am. J. Obstet. Gynecol."},{"issue":"4","key":"18_CR7","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s42979-021-00694-6","volume":"2","author":"M Fasihi","year":"2021","unstructured":"Fasihi, M., Nadimi-Shahraki, M.H., Jannesari, A.: A shallow 1-D convolution neural network for fetal state assessment based on cardiotocogram. SN Comput. Sci. 2(4), 287 (2021)","journal-title":"SN Comput. Sci."},{"key":"18_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104008","volume":"78","author":"Y Fei","year":"2022","unstructured":"Fei, Y., et al.: Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units. Biomed. Signal Process. Control 78, 104008 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Georgoulas, G.G., Stylios, C.D., Nokas, G., Groumpos, P.P.: Classification of fetal heart rate during labour using hidden Markov models. In: 2004 IEEE International Joint Conference on Neural Networks, vol. 3, pp. 2471\u20132475. IEEE (2004)","DOI":"10.1109\/IJCNN.2004.1381017"},{"issue":"1","key":"18_CR10","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/S0020-7292(01)00545-8","volume":"76","author":"B Leszczynska-Gorzelak","year":"2002","unstructured":"Leszczynska-Gorzelak, B., Poniedzialek-Czajkowska, E., Oleszczuk, J.: Intrapartum cardiotocography and fetal pulse oximetry in assessing fetal hypoxia. Int. J. Gynecol. Obstet. 76(1), 9\u201314 (2002)","journal-title":"Int. J. Gynecol. Obstet."},{"issue":"2","key":"18_CR11","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1109\/JIOT.2018.2845128","volume":"6","author":"J Li","year":"2018","unstructured":"Li, J., et al.: Automatic classification of fetal heart rate based on convolutional neural network. IEEE Internet Things J. 6(2), 1394\u20131401 (2018)","journal-title":"IEEE Internet Things J."},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Liang, S., Li, Q.: Automatic evaluation of fetal heart rate based on deep learning. In: 2021 2nd Information Communication Technologies Conference (ICTC), pp. 235\u2013240. IEEE (2021)","DOI":"10.1109\/ICTC51749.2021.9441583"},{"key":"18_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115714","volume":"186","author":"M Liu","year":"2021","unstructured":"Liu, M., Lu, Y., Long, S., Bai, J., Lian, W.: An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification. Expert Syst. Appl. 186, 115714 (2021)","journal-title":"Expert Syst. Appl."},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.bpobgyn.2015.02.009","volume":"30","author":"I Nunes","year":"2016","unstructured":"Nunes, I., Ayres-de Campos, D.: Computer analysis of foetal monitoring signals. Best Pract. Res. Clin. Obstet. Gynaecol. 30, 68\u201378 (2016)","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"issue":"1","key":"18_CR15","doi-asserted-by":"publisher","first-page":"13367","DOI":"10.1038\/s41598-021-92805-9","volume":"11","author":"J Ogasawara","year":"2021","unstructured":"Ogasawara, J., et al.: Deep neural network-based classification of cardiotocograms outperformed conventional algorithms. Sci. Rep. 11(1), 13367 (2021)","journal-title":"Sci. Rep."},{"key":"18_CR16","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR 2015) (2015)"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Spairani, E., Daniele, B., Signorini, M.G., Magenes, G.: A deep learning mixed- data type approach for the classification of FHR signals. Front. Bioeng. Biotechnol. 10 (2022)","DOI":"10.3389\/fbioe.2022.887549"},{"key":"18_CR18","doi-asserted-by":"publisher","first-page":"2506","DOI":"10.3389\/fphys.2022.969052","volume":"13","author":"Y Xiao","year":"2022","unstructured":"Xiao, Y., Lu, Y., Liu, M., Zeng, R., Bai, J.: A deep feature fusion network for fetal state assessment. Front. Physiol. 13, 2506 (2022)","journal-title":"Front. Physiol."},{"issue":"1","key":"18_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13052-017-0382-8","volume":"43","author":"V Zanardo","year":"2017","unstructured":"Zanardo, V., De Luca, F., Simbi, A.K., Parotto, M., Guerrini, P., Straface, G.: Umbilical cord blood acid-base analysis and the development of significant hyper-bilirubinemia in near-term and term newborns: a cohort study. Ital. J. Pediatr. 43(1), 1\u20135 (2017)","journal-title":"Ital. J. Pediatr."},{"key":"18_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-1007-5","volume":"19","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Deng, Y., Zhang, Y., Zhang, Y., Zhang, X., Shao, L.: DeepFHR: intelligent prediction of fetal acidemia using fetal heart rate signals based on convolutional neural network. BMC Med. Inform. Decis. Mak. 19, 1\u201315 (2019)","journal-title":"BMC Med. Inform. Decis. Mak."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4742-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:05:25Z","timestamp":1729847125000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4742-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947416","9789819947423"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4742-3_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}