Shortcoming of Visual Interpretation of Cardiotocography: A Comparative Study with Automated Method and Established Guideline Using Statistical Analysis-Reference-Cited by-同舟云学术

Shortcoming of Visual Interpretation of Cardiotocography: A Comparative Study with Automated Method and Established Guideline Using Statistical Analysis

Published:2020-05 Issue:3 Volume:1 Page:
ISSN:2662-995X
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Das Sahana,Mukherjee Himadri,Roy Kaushik^ORCID,Saha Chanchal K.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-020-00188-x.pdf

Reference34 articles.

1. Ham J, Van den Bos K. The merits of unconscious processing of directly and indirectly obtained information about social justice. Soc Cognit. 2010;28:180–90.

2. Darrigo M. Clinical decision making by critical care mid-level practitioners working within an interdisciplinary team: a dissertation. Graduate School of Nursing Dissertations; 2009. https://doi.org/10.13028/s3v6-1y09. Retrieved from https://escholarship.umassmed.edu/gsn_diss/12.

3. Cader R, Campbell S, Watson D. Cognitive continuum theory in nursing decision-making. J Adv Nurs. 2005;49(4):397–405.

4. Guijjaro-Berdinas B, Alonso-Betanzos A, Fontella-Romero O. Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system. Artif Intell. 2002;136(1):1–27.

5. Robinson B. A Review of NICHD standardized nomenclature for cardiotocography: the importance of speaking a common language when describing electronic fetal monitoring. Rev Obstet Gynecol. 2008;1(2):56–60.

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence based cardiotocogram assessment during labor;European Journal of Obstetrics & Gynecology and Reproductive Biology;2024-04

2. Machine learning on cardiotocography data to classify fetal outcomes: A scoping review;Computers in Biology and Medicine;2024-04

3. Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24

4. A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set;Scientific Reports;2023-02-13

5. A Sensor Fault Identification Method Based on Adaptive Particle Swarm Optimization Support Vector Machine;Proceedings of IncoME-VI and TEPEN 2021;2022-09-18