Literature DB >> 26211832

Computer analysis of foetal monitoring signals.

Inês Nunes1, Diogo Ayres-de-Campos2.   

Abstract

Five systems for computer analysis of foetal monitoring signals are currently available, incorporating the evaluation of cardiotocographic (CTG) or combined CTG with electrocardiographic ST data. All systems have been integrated with central monitoring stations, allowing the simultaneous monitoring of several tracings on the same computer screen in multiple hospital locations. Computer analysis elicits real-time visual and sound alerts for health care professionals when abnormal patterns are detected, with the aim of prompting a re-evaluation and subsequent clinical action, if considered necessary. Comparison between the CTG analyses provided by the computer and clinical experts has been carried out in all systems, and in three of them, the accuracy of computer alerts in predicting newborn outcomes was evaluated. Comparisons between these studies are hampered by the differences in selection criteria and outcomes. Two of these systems have just completed multicentre randomised clinical trials comparing them with conventional CTG monitoring, and their results are awaited shortly. For the time being, there is limited evidence regarding the impact of computer analysis of foetal monitoring signals on perinatal indicators and on health care professionals' behaviour.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  cardiotocography; computer-assisted; foetal; foetal monitoring; heart rate; signal processing

Mesh:

Year:  2015        PMID: 26211832     DOI: 10.1016/j.bpobgyn.2015.02.009

Source DB:  PubMed          Journal:  Best Pract Res Clin Obstet Gynaecol        ISSN: 1521-6934            Impact factor:   5.237


  3 in total

1.  A systematic review of automated pre-processing, feature extraction and classification of cardiotocography.

Authors:  Shahad Al-Yousif; Ariep Jaenul; Wisam Al-Dayyeni; Ah Alamoodi; Ihab Jabori; Nooritawati Md Tahir; Ali Amer Ahmed Alrawi; Zafer Cömert; Nael A Al-Shareefi; Abbadullah H Saleh
Journal:  PeerJ Comput Sci       Date:  2021-04-27

Review 2.  DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.

Authors:  Zhidong Zhao; Yanjun Deng; Yang Zhang; Yefei Zhang; Xiaohong Zhang; Lihuan Shao
Journal:  BMC Med Inform Decis Mak       Date:  2019-12-30       Impact factor: 2.796

3.  Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters.

Authors:  Javier Esteban-Escaño; Berta Castán; Sergio Castán; Marta Chóliz-Ezquerro; César Asensio; Antonio R Laliena; Gerardo Sanz-Enguita; Gerardo Sanz; Luis Mariano Esteban; Ricardo Savirón
Journal:  Entropy (Basel)       Date:  2021-12-30       Impact factor: 2.524

  3 in total

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