dc.contributor.author | Marnani, Roya Aghadavoud | |
dc.contributor.author | Jaroš, René | |
dc.contributor.author | Pavlíček, Jan | |
dc.contributor.author | Martinek, Radek | |
dc.contributor.author | Vilímková Kahánková, Radana | |
dc.date.accessioned | 2024-11-15T08:28:14Z | |
dc.date.available | 2024-11-15T08:28:14Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | IEEE Access. 2024, vol. 12, p. 44730-44747. | cs |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | http://hdl.handle.net/10084/155303 | |
dc.description.abstract | Non-invasive electrocardiography (NI-ECG) has become an indispensable tool for monitoring fetal and neonatal cardiac activity throughout the stages of pregnancy and postpartum care. This review emphasizes the distinct advantages of NI-ECG, including extended monitoring capabilities and valuable insights into fetal and neonatal health. The exploration of textile electrodes is highlighted as a promising alternative, offering improved comfort and reduced skin irritation compared to traditional adhesive electrodes. However, challenges in NI-ECG persist, with electrode placement, quantity, and noise removal being key considerations. The review underscores the significance of addressing interference sources, such as maternal and fetal body signals, motion artifacts, and electrode-skin contact. Additionally, the discussion extends to computer-aided diagnostics, presenting novel approaches for classifying fetal and neonatal health during pregnancy and delivery. Ongoing research aims to optimize electrode placement, develop advanced noise reduction algorithms, and explore sophisticated classification methodologies. These advancements hold the potential to enhance electronic monitoring, enabling early detection of abnormalities and promoting improved outcomes in prenatal and neonatal care. | cs |
dc.language.iso | en | cs |
dc.publisher | IEEE | cs |
dc.relation.ispartofseries | IEEE Access | cs |
dc.relation.uri | https://doi.org/10.1109/ACCESS.2024.3378747 | cs |
dc.rights | © 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. | cs |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | cs |
dc.subject | abdominal monitoring | cs |
dc.subject | fetal electrocardiography | cs |
dc.subject | fetal heart rate monitoring | cs |
dc.subject | neonatal electrocardiography | cs |
dc.subject | non-invasive monitoring | cs |
dc.subject | signal processing | cs |
dc.subject | textile electrodes | cs |
dc.subject | artificial intelligence | cs |
dc.subject | classification algorithm | cs |
dc.subject | deep learning | cs |
dc.subject | machine learning | cs |
dc.title | Advancements and challenges in non-invasive electrocardiography for prenatal, intrapartum, and postnatal care: A comprehensive review | cs |
dc.type | article | cs |
dc.identifier.doi | 10.1109/ACCESS.2024.3378747 | |
dc.rights.access | openAccess | cs |
dc.type.version | publishedVersion | cs |
dc.type.status | Peer-reviewed | cs |
dc.description.source | Web of Science | cs |
dc.description.volume | 12 | cs |
dc.description.lastpage | 44747 | cs |
dc.description.firstpage | 44730 | cs |
dc.identifier.wos | 001193804200001 | |