Late pregnancy analysis with Yunban’s remote fetal monitoring system

With the adoption of the two-child policy, there has been a large increase in women of older maternal and high-risk pregnant women. So, it is necessary to analyze the health status of women in the late pregnancy on time. To analyze the effect on using remote fetal monitoring on women in the late pre...

Full description

Saved in:
Bibliographic Details
Main Authors: Qiuping Wang, Weihua Yang, Lie Li, Guokai Yan, Huihui Wang, Jianqiang Li
Format: Article
Language:English
Published: Wiley 2019-03-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/1550147719832835
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832547912539701248
author Qiuping Wang
Weihua Yang
Lie Li
Guokai Yan
Huihui Wang
Jianqiang Li
author_facet Qiuping Wang
Weihua Yang
Lie Li
Guokai Yan
Huihui Wang
Jianqiang Li
author_sort Qiuping Wang
collection DOAJ
description With the adoption of the two-child policy, there has been a large increase in women of older maternal and high-risk pregnant women. So, it is necessary to analyze the health status of women in the late pregnancy on time. To analyze the effect on using remote fetal monitoring on women in the late pregnancy, we selected women in the late stage of pregnancy in our hospital as research subjects. They were randomly divided into two groups: the experimental group, which engaged in remote fetal monitoring, and the control group, which adopted traditional cardiac monitoring. In order to get more effective data, we used the Kalman filter and audio repair algorithms to preprocess the collected data. During follow-up observation, we compared the two groups using neonatal cardiac monitoring by employing the non-stress test and observed the occurrence of neonatal asphyxia. The incidence of neonatal abnormal non-stress test in the experimental group and the control group was 33.6% and 17.3%, respectively; the difference was statistically significant ( p  < 0.05). The incidence of neonatal asphyxia in the experimental group was 12.5%, which was significantly lower than in the control group (30%; p  < 0.05). We have found that women in the late stage of pregnancy who adopted remote fetal monitoring could detect abnormal non-stress test earlier and thus increase in the detection of rate of neonatal asphyxia.
format Article
id doaj-art-4dad1838095949169b0f7f2dbae15bf7
institution Kabale University
issn 1550-1477
language English
publishDate 2019-03-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-4dad1838095949169b0f7f2dbae15bf72025-02-03T06:43:00ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-03-011510.1177/1550147719832835Late pregnancy analysis with Yunban’s remote fetal monitoring systemQiuping Wang0Weihua Yang1Lie Li2Guokai Yan3Huihui Wang4Jianqiang Li5University of Chinese Academy of Sciences ShenZhen Hospital (GuangMing), Shenzhen, P.R. ChinaUniversity of Chinese Academy of Sciences ShenZhen Hospital (GuangMing), Shenzhen, P.R. ChinaUniversity of Chinese Academy of Sciences ShenZhen Hospital (GuangMing), Shenzhen, P.R. ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, P.R. ChinaDepartment of Engineering, Jacksonville University, Jacksonville, FL, USACollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, P.R. ChinaWith the adoption of the two-child policy, there has been a large increase in women of older maternal and high-risk pregnant women. So, it is necessary to analyze the health status of women in the late pregnancy on time. To analyze the effect on using remote fetal monitoring on women in the late pregnancy, we selected women in the late stage of pregnancy in our hospital as research subjects. They were randomly divided into two groups: the experimental group, which engaged in remote fetal monitoring, and the control group, which adopted traditional cardiac monitoring. In order to get more effective data, we used the Kalman filter and audio repair algorithms to preprocess the collected data. During follow-up observation, we compared the two groups using neonatal cardiac monitoring by employing the non-stress test and observed the occurrence of neonatal asphyxia. The incidence of neonatal abnormal non-stress test in the experimental group and the control group was 33.6% and 17.3%, respectively; the difference was statistically significant ( p  < 0.05). The incidence of neonatal asphyxia in the experimental group was 12.5%, which was significantly lower than in the control group (30%; p  < 0.05). We have found that women in the late stage of pregnancy who adopted remote fetal monitoring could detect abnormal non-stress test earlier and thus increase in the detection of rate of neonatal asphyxia.https://doi.org/10.1177/1550147719832835
spellingShingle Qiuping Wang
Weihua Yang
Lie Li
Guokai Yan
Huihui Wang
Jianqiang Li
Late pregnancy analysis with Yunban’s remote fetal monitoring system
International Journal of Distributed Sensor Networks
title Late pregnancy analysis with Yunban’s remote fetal monitoring system
title_full Late pregnancy analysis with Yunban’s remote fetal monitoring system
title_fullStr Late pregnancy analysis with Yunban’s remote fetal monitoring system
title_full_unstemmed Late pregnancy analysis with Yunban’s remote fetal monitoring system
title_short Late pregnancy analysis with Yunban’s remote fetal monitoring system
title_sort late pregnancy analysis with yunban s remote fetal monitoring system
url https://doi.org/10.1177/1550147719832835
work_keys_str_mv AT qiupingwang latepregnancyanalysiswithyunbansremotefetalmonitoringsystem
AT weihuayang latepregnancyanalysiswithyunbansremotefetalmonitoringsystem
AT lieli latepregnancyanalysiswithyunbansremotefetalmonitoringsystem
AT guokaiyan latepregnancyanalysiswithyunbansremotefetalmonitoringsystem
AT huihuiwang latepregnancyanalysiswithyunbansremotefetalmonitoringsystem
AT jianqiangli latepregnancyanalysiswithyunbansremotefetalmonitoringsystem