ArticleComputational and mathematical methods in medicine2018
An Improved FastICA Method for Fetal ECG Extraction.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques.Sensors (Basel, Switzerland) · 2025Review
- Template subtraction based methods for non-invasive fetal electrocardiography extraction.Scientific reports · 2024Article
- [Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Independent component analysis algorithms for non-invasive fetal electrocardiography.PloS one · 2023Article
- Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature.IEEE sensors letters · 2022Article
- Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis.Annals of biomedical engineering · 2022Article
- Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition.Sensors (Basel, Switzerland) · 2022Article
- A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction.PloS one · 2021Article
- A Fetal ECG Monitoring System Based on the Android Smartphone.Sensors (Basel, Switzerland) · 2019Article
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4 authors.
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Abstract
objectiveThe fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG.
methodsFirst, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton's iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal.
resultsAn annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean (
conclusionsThe proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.
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