Ambulatory fetal ECG monitoring

2017 ◽  
Vol 12 (13) ◽  
pp. 75-82
Author(s):  
K.V. Nasedkin ◽  
◽  
V.V. Fedotenko ◽  
O.G. Viunytskyi ◽  
V.I. Shulgin ◽  
...  
Keyword(s):  
Author(s):  
J. Karin ◽  
M. Hirsch ◽  
O. Segal ◽  
S. Akselrod

2002 ◽  
Vol 13 (02) ◽  
Author(s):  
Jenny A Westgate ◽  
Laura Bennet ◽  
Alistair Gunn
Keyword(s):  

2008 ◽  
Vol 87 (11) ◽  
pp. 1189-1193 ◽  
Author(s):  
Anita Kale ◽  
Yap-Seng Chong ◽  
Arijit Biswas
Keyword(s):  

2015 ◽  
Vol 9 (2) ◽  
pp. 237-247 ◽  
Author(s):  
Shuang Song ◽  
Michael Rooijakkers ◽  
Pieter Harpe ◽  
Chiara Rabotti ◽  
Massimo Mischi ◽  
...  

PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e28129 ◽  
Author(s):  
Zubair Rauf ◽  
Ediri O'Brien ◽  
Tamara Stampalija ◽  
Florin P. Ilioniu ◽  
Tina Lavender ◽  
...  

2019 ◽  
Vol 220 (1) ◽  
pp. S42-S43
Author(s):  
Martha Monson ◽  
Cara Heuser ◽  
Greg Snow ◽  
Michael Varner ◽  
Sean Esplin

2016 ◽  
pp. 1-1 ◽  
Author(s):  
Shuang Song ◽  
Michael Rooijakkers ◽  
Pieter Harpe ◽  
Chiara Rabotti ◽  
Massimo Mischi ◽  
...  

2013 ◽  
Vol 749 ◽  
pp. 250-253 ◽  
Author(s):  
Wen Po Yao ◽  
Jun Chang Zhao ◽  
Zheng Zhong Zheng ◽  
Tie Bing Liu ◽  
Hong Xing Liu ◽  
...  

Fetal electrocardiogram (FECG) separation gets widely attention due to its clinical significance. In the paper, we proposed an improved robust independent component analysis for fetal ECG separation. Firstly, wavelet decomposition was applied to fetal ECG to get the relevant parameters. Then, the RobustICA was used to separate the mixed signals. Compared to robust independent component analysis, computing speed of the improved algorithm increased by an average of 15 percent while minimum mean square error fluctuations 0.0008, which indicated that this algorithm could be effectively used in clinical fetal ECG monitoring.


Sign in / Sign up

Export Citation Format

Share Document