A Novel Approach for Cardiotocography Paper Digitization and Classification for Abnormality Detection-Reference-Cited by-同舟云学术

A Novel Approach for Cardiotocography Paper Digitization and Classification for Abnormality Detection

Published:2023 Issue: Volume:11 Page:42521-42533
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Öztürk Sibel¹,Şahin Safiye Ağapinar¹,Aksoy Ayşe Nur²,Ari Berna³^ORCID,Akinbi Alex⁴^ORCID

Affiliation:

1. Department of Midwifery, Atatürk University, Erzurum, Turkey

2. Department of Obstetrics and Gynecology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey

3. Department of Electrical and Electronics Engineering, Firat University, Elâziğ, Turkey

4. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, U.K

Funder

Ataturk University and Erzurum City Hospital Turkiye Participatory Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10109722.pdf?arnumber=10109722

Reference36 articles.

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2. Optimized residual convolutional learning neural network for intrapartum maternal-embryo risk assessment;parvathavarthine;Eur J Mol Clin Med,2020

3. Low dimensional manifold embedding for scattering coefficients of intrapartum fetale heart rate variability;chudáèek;Proc 36th Annu Int Conf IEEE Eng Med Biol Soc,2014

4. Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study

5. Efficient approach for digitization of the cardiotocography signals

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