PMID- 36374955
OWN - NLM
STAT- MEDLINE
DCOM- 20221116
LR  - 20230125
IS  - 1943-619X (Electronic)
IS  - 0278-940X (Linking)
VI  - 50
IP  - 3
DP  - 2022
TI  - A Fetal ECG Extraction Method Based on ELM Optimized by Improved PSO Algorithm.
PG  - 35-47
LID - 10.1615/CritRevBiomedEng.2022044778 [doi]
AB  - The extraction of fetal electrocardiogram (FECG) is of great significance for 
      perinatal fetal monitoring. In order to improve the prediction accuracy of FECG, 
      a FECG extraction method based on extreme learning machine (ELM) optimized by an 
      improved particle swarm optimization (IPSO) was proposed (IPSO-ELM). First, 
      according to the characteristics of the mixed signal on the maternal abdominal 
      wall, and based on the global search ability of IPSO, the initial weight matrix 
      and hidden layer bias of ELM were optimized to match with the mixed signal of the 
      maternal abdominal wall and the network topology. Then, an ELM model was 
      established using the optimal network parameters obtained by IPSO. The nonlinear 
      transformation of the maternal ECG (MECG) signal to the abdominal wall was 
      estimated by the IPSO-ELM network. Finally, the non-linearly transformed MECG 
      signal was mixed with the abdominal wall subtract to obtain clear FECG. The 
      experimental results of clinical ECG signals in DaISy dataset showed that, 
      compared with the traditional normalized minimum mean square error, support 
      vector machine method, and ELM neural network methods, the proposed method can 
      extract clearer FECG signals and improve the signal-to-noise ratio of extracted 
      FECG.
FAU - Chen, Jiqin
AU  - Chen J
AD  - All-Purpose Quality Education College, Wuchang University of Technology, Wuhan, 
      430065, China.
FAU - Cao, Fenglin
AU  - Cao F
AD  - All-Purpose Quality Education College, Wuchang University of Technology, Wuhan, 
      430065, China.
FAU - Gao, Ping
AU  - Gao P
AD  - All-Purpose Quality Education College, Wuchang University of Technology, Wuhan, 
      430065, China.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Crit Rev Biomed Eng
JT  - Critical reviews in biomedical engineering
JID - 8208627
SB  - IM
MH  - Humans
MH  - Pregnancy
MH  - Female
MH  - *Signal Processing, Computer-Assisted
MH  - *Electrocardiography/methods
MH  - Fetal Monitoring/methods
MH  - Algorithms
MH  - Neural Networks, Computer
EDAT- 2022/11/15 06:00
MHDA- 2022/11/18 06:00
CRDT- 2022/11/14 14:16
PHST- 2022/11/14 14:16 [entrez]
PHST- 2022/11/15 06:00 [pubmed]
PHST- 2022/11/18 06:00 [medline]
AID - 771523343910ccc2,16d9e89264c37843 [pii]
AID - 10.1615/CritRevBiomedEng.2022044778 [doi]
PST - ppublish
SO  - Crit Rev Biomed Eng. 2022;50(3):35-47. doi: 10.1615/CritRevBiomedEng.2022044778.