{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:43:18Z","timestamp":1774539798557,"version":"3.50.1"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T00:00:00Z","timestamp":1543795200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program of China","award":["2016YFC0904902"],"award-info":[{"award-number":["2016YFC0904902"]}]},{"name":"National Key R&D Program of China","award":["61502032"],"award-info":[{"award-number":["61502032"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2016YFC0904902"],"award-info":[{"award-number":["2016YFC0904902"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502032"],"award-info":[{"award-number":["61502032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2018,12,3]]},"abstract":"<jats:p>Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named \u201cMKNet\u201d and recurrent neural network named \u201cMKRNN\u201d are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.<\/jats:p>","DOI":"10.1155\/2018\/8568617","type":"journal-article","created":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T18:31:38Z","timestamp":1543861898000},"page":"1-12","source":"Crossref","is-referenced-by-count":29,"title":["The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network"],"prefix":"10.1155","volume":"2018","author":[{"given":"Haijing","family":"Tang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 10081, China"}]},{"given":"Taoyi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 10081, China"}]},{"given":"Mengke","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 10081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7983-6473","authenticated-orcid":true,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 10081, China"}]}],"member":"311","reference":[{"key":"1","first-page":"139","volume":"40","year":"2010","journal-title":"Australian and New Zealand Journal of Obstetrics and Gynaecology"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000000207"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1177\/1357633x9600200207"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/s1470-0328(02)01988-2"},{"key":"5","first-page":"510","volume":"37","year":"2010","journal-title":"Journal of Obstetric Gynecologic and Neonatal Nursing"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(72)90093-2"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.2478\/v10136-012-0031-x"},{"key":"8","first-page":"19","volume":"47","year":"2013","journal-title":"International Journal of Computer Applications"},{"key":"9","year":"2014"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/s0031-3203(96)00142-2"},{"key":"12","first-page":"391","volume-title":"Accelerating the Super-5 Resolution Convolutional Neural Network","year":"2016"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.2307\/3003508"},{"key":"15","first-page":"1929","volume":"15","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"16","year":"1980"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925x-10-92"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/30\/8\/001"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2004.03.014"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(75)90528-1"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2011.06.008"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"24","volume-title":"Convolutional neural networks","year":"2017"},{"key":"25","first-page":"153","volume":"5","year":"2009","journal-title":"Immunology of Fungal Infections"},{"key":"26","volume-title":"Understanding and working with Keras","year":"2018"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/8568617.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/8568617.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/8568617.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T18:31:42Z","timestamp":1543861902000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2018\/8568617\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,3]]},"references-count":23,"alternative-id":["8568617","8568617"],"URL":"https:\/\/doi.org\/10.1155\/2018\/8568617","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,3]]}}}