Key result
IPSO-ELM method extracts clearer fetal ECG signals and improves signal-to-noise ratio over traditional methods.
Why the study?
Fetal ECG extraction is clinically significant for perinatal fetal monitoring, motivating methods to improve extraction prediction accuracy.
Does the IPSO-ELM algorithm improve the signal-to-noise ratio of fetal ECG extraction from maternal abdominal signals compared to traditional methods?
Does the IPSO-ELM algorithm improve the signal-to-noise ratio of fetal ECG extraction from maternal abdominal signals compared to traditional methods?
The IPSO-ELM algorithm offers an improved computational approach for extracting high-quality fetal ECG signals from non-invasive maternal abdominal recordings.
May aid non-invasive fetal monitoring; leaves open clinical validation before adoption.
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.
No takes yet. Share an insight, caveat, or question.
Chen et al. (2022) studied Fetal electrocardiogram extraction. IPSO-ELM method vs. Traditional normalized minimum mean square error, support vector machine, and ELM neural network methods was evaluated on Signal-to-noise ratio and clarity of extracted FECG. An IPSO-ELM method extracted clearer fetal ECG signals and improved the signal-to-noise ratio compared with traditional normalized minimum mean square error, SVM, and standard ELM methods.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: