Automation in clinical data collection in obstetrics - Enabling pooled IPD studies for fetal heart rate and activity monitoring
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Data Mining and Machine Learning, Data Science, Software Engineering
- Keywords
- obstetrics, Cardiotocography, fetal heart rate, activity monitoring, data collection, IPD, studies, Software, umbilical arterial blood, pH
- Copyright
- © 2016 Weber et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Automation in clinical data collection in obstetrics - Enabling pooled IPD studies for fetal heart rate and activity monitoring. PeerJ Preprints 4:e2098v1 https://doi.org/10.7287/peerj.preprints.2098v1
Abstract
Cardiotocography is currently the standard surveillance tool during labour. For this reason, there are large cardiotography datasets available. Several studies gathering additional information of computerized CTG data aiming to improve surveillance during delivery and birth outcome are currently performed. Manual data collection and analysis takes a lot of human resources, is error-prone and study parameters can hardly be adjusted later on. Therefore, a software based approach was chosen to minimize the effort of preparation. The software called “CTG and patient information matcher” (CAPIM) collects relevant CTG signals for a specified patient dataset and under recognition of several parameters. CAPIM was tested with the patient database of Frauenklinik und Poliklinik of Technische Universität München (Munich, Germany). Further hospitals will follow. The cases received from CAPIM will be used to do feasibility studies for two innovative signal processing techniques as „phase-rectified signal averaging“ method on fetal heart rate raw data and „deceleration area“ method to improve prediction of birth outcome.
Author Comment
This poster is a contribution to the 3rd Winter Symposium of the "Human Motion Project" and is part of "PeerJ Human Motion Project collection".