scholarly journals Non-invasive Detection and Compression of Fetal Electrocardiogram

2017 ◽  
Author(s):  
Xin Gao
Keyword(s):  
Fetal Electrocardiogram ◽  
Non Invasive

scholarly journals Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 392-412
Author(s):  
Geetika Aggarwal ◽  
Yang Wei
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Skin Penetration ◽  
Future Research ◽  
Fetal Electrocardiogram ◽  
Smart Textiles ◽  
Non Invasive ◽  
Future Research Directions ◽  
Electrocardiogram Monitoring

During the pregnancy, fetal electrocardiogram (FECG) is deployed to analyze fetal heart rate (FHR) of the fetus to indicate the growth and health of the fetus to determine any abnormalities and prevent diseases. The fetal electrocardiogram monitoring can be carried out either invasively by placing the electrodes on the scalp of the fetus, involving the skin penetration and the risk of infection, or non-invasively by recording the fetal heart rate signal from the mother’s abdomen through a placement of electrodes deploying portable, wearable devices. Non-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance because of the comfort to the pregnant women and being achieved remotely, specifically in the unprecedented circumstances such as pandemic or COVID-19. Textiles have been at the heart of human technological progress for thousands of years, with textile developments closely tied to key inventions that have shaped societies. The relatively recent invention of smart textiles is set to push boundaries again and has already opened the potential for garments relevant to medicine, and health monitoring. This paper aims to discuss the different technologies and methods used in non-invasive fetal electrocardiogram (NIFECG) monitoring as well as the potential and future research directions of NIFECG in the smart textiles area.

scholarly journals Fetal Electrocardiogram Extraction from the Mother’s Abdominal Signal Using the Ensemble Kalman Filter

2021 ◽  
Author(s):  
Sadaf Sarafan ◽  
Tai Le ◽  
Michael P.H. Lau ◽  
Afshan Hameed ◽  
Tadesse Ghirmai ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Congenital Heart ◽  
Single Channel ◽  
Heart Defects ◽  
Qrs Complex ◽  
Fetal Electrocardiogram ◽  
High Noise ◽  
Non Invasive ◽  
Novel Algorithm

AbstractFetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e. in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman Filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed Extended Kalman Filter (EKF) based algorithm.

scholarly journals A systematic review of cardiac time intervals utilising non-invasive fetal electrocardiogram in normal fetuses

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Vinayak Smith ◽  
Senthuran Arunthavanathan ◽  
Amrish Nair ◽  
Diane Ansermet ◽  
Fabricio da Silva Costa ◽  
...  
Keyword(s):  
Systematic Review ◽  
Time Intervals ◽  
Fetal Electrocardiogram ◽  
Non Invasive ◽  
Cardiac Time Intervals

Fetal Electrocardiogram Extraction Based on SWT-MM Method

2014 ◽  
Vol 644-650 ◽  
pp. 4415-4421
Author(s):  
Qiong Liao ◽  
Jie Luo ◽  
Yang Liu
Keyword(s):  
Time Use ◽  
Real Data ◽  
Differential Threshold ◽  
Stationary Wavelet Transform ◽  
Threshold Method ◽  
Fetal Electrocardiogram ◽  
Automated Control ◽  
Non Invasive ◽  
Average Accuracy ◽  
Modulus Maxima

Fetal electrocardiogram (FECG) is of great importance due to the potentially precise information that FECG carries could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. In this paper, a method based on combined Stationary Wavelet Transform and Modulus-Maxima (SWT-MM) method is proposed for extracting the complete morphology of the FECG from maternal abdominal ECG (AECG). It particularly provides a different way of constructing the maternal ECG (MECG) template. The Efficacy of the method was validated using real data in Non-Invasive Fetal Electrocardiogram Database. The morphology of the extracted FECG was clearly seen that the fetal R-peak detection by simple differential-threshold method acquired the average accuracy of 96.8%. The method provides additional important benefits of fast speed and automated control for applying into the fetal monitors. Therefore, the method is potentially a strong tool for FECG extraction, especially in real-time use.

scholarly journals A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator

2016 ◽  
Vol 37 (2) ◽  
pp. 238-256 ◽  
Author(s):  
Radek Martinek ◽  
Michal Kelnar ◽  
Petr Koudelka ◽  
Jan Vanus ◽  
Petr Bilik ◽  
...  
Keyword(s):  
Signal Generator ◽  
Fetal Electrocardiogram ◽  
Non Invasive ◽  
Electrocardiogram Signal

scholarly journals Non-invasive recordings of fetal electrocardiogram during pregnancy using electric potential sensors

AIP Advances ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 105114 ◽  
Author(s):  
E. Rendon-Morales ◽  
R. J. Prance ◽  
R. Aviles-Espinosa
Keyword(s):  
Electric Potential ◽  
Fetal Electrocardiogram ◽  
Non Invasive
Sign in / Sign up

Export Citation Format

Share Document