Results 21 to 30 of about 349,300 (234)

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram [PDF]

open access: yes, 2023
Non-invasive maternal electrocardiogram recording is the least unpleasant method to record a weak fetal electrocardiogram signal. The importance of this recording lies in the fact that it reveals crucial information about the fetal health state ...
Boualem Kazed   +5 more
core   +1 more source

Modified block compressed sensing for extraction of fetal electrocardiogram from mother electrocardiogram using block compressed sensing based guided FOCUSS and FAST-independent component / [PDF]

open access: yes, 2021
Fetal electrocardiogram extraction from abdominal electrocardiogram is perilous task for tele-monitoring of fetus which require in-depth understanding.
Awan, Muhammad Tayyib   +11 more
core   +1 more source

Fetal Electrocardiogram Signal Modelling Using Genetic Algorithm [PDF]

open access: yes, 2007
The fetal electrocardiogram signal (FECG) is a major tool in monitoring and diagnosis of the fetus high risk conditions and arrhythmias. This paper introduces an accurate mathematical model for fetal electrocardiogram based on a real template FECG signal.
Kianoush Nazarpour   +5 more
core   +3 more sources

Non-invasive fetal electrocardiogram (NI-FECG) showing both maternal and fetal R-waves.

open access: yes, 2022
Non-invasive fetal electrocardiogram (NI-FECG) showing both maternal and fetal R-waves.
John Hansen (5749535)   +4 more
core   +1 more source

Normal ranges for fetal electrocardiogram values for the healthy fetus of 18-24 weeks of gestation: a prospective cohort study. [PDF]

open access: yesBMC Pregnancy Childbirth, 2016
Background: The fetal anomaly ultrasound only detects 65 to 81 % of the patients with congenital heart disease, making it the most common structural fetal anomaly of which a significant part is missed during prenatal life.
Verdurmen KMJ   +6 more
europepmc   +2 more sources

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

open access: yesBig Data Mining and Analytics, 2023
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead. It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA ...
Said Ziani   +2 more
doaj   +1 more source

A clustering-based method for single-channel fetal heart rate monitoring. [PDF]

open access: yesPLoS ONE, 2018
Non-invasive fetal electrocardiography (ECG) is based on the acquisition of signals from abdominal surface electrodes. The composite abdominal signal consists of the maternal electrocardiogram along with the fetal electrocardiogram and other electrical ...
Encarnación Castillo   +5 more
doaj   +1 more source

The non-invasive fetal electrocardiogram (NI-fECG) setup with electrodes on the maternal abdomen.

open access: yes, 2022
The non-invasive fetal electrocardiogram (NI-fECG) setup with electrodes on the maternal abdomen.
E. R. van den Heuvel (10005970)   +10 more
core   +1 more source

The influence of betamethasone on fetal heart rate variability, obtained by non-invasive fetal electrocardiogram recordings [PDF]

open access: yes, 2018
Background: Betamethasone is widely used to enhance fetal lung maturation in case of threatened preterm labour. Fetal heart rate variability is one of the most important parameters to assess in fetal monitoring, since it is a reliable indicator for fetal
Dieleman, Jeanne P.   +26 more
core   +2 more sources

Quality of fetal heart rate monitoring with transabdominal fetal ECG during maternal movement in labor: A prospective study

open access: yesActa Obstetricia et Gynecologica Scandinavica, 2022
Introduction Transabdominal electrocardiographic (TAfECG) acquisition of fetal heart rate (FHR) signals has recently been introduced into leading commercial cardiotocographic (CTG) monitors.
Catarina Reis‐de‐Carvalho   +2 more
doaj   +1 more source

Home - About - Disclaimer - Privacy