Fetal Electrocardiography Extraction Based on Improved Fast Independent Components Analysis Algorithm

Aiming at the problem that the fast independent components analysis (ICA) algorithm is sensitive to the initial value and easy to fall into local optimization, an improved fast ICA method is proposed for the extraction of fetal echocardiography (FECG) by combining the Simpson-Newton iterative and chaotic optimization algorithm to replace the traditional Newton iterative method. First, the Simpson formula is used to modify the traditional Newton method and a Simpson-Newton iterative algorithm is constructed. It shows that the Simpson-Newton iterative algorithm can significantly reduce the sensitivity of initial value selection, and has faster convergence speed. Then, combined with the Simpson-Newton method, the chaos optimization algorithm can obtain the approximate global optimal solution, which solves the problem that the traditional Newton iterative method tends to fall into local optimal and improves the separation performance of the fast ICA algorithm. Finally, based on chaos optimization, the proposed Simpson-Newton iterative fast ICA algorithm is applied to the extraction of FECG signals, and the extraction effect is evaluated by visual waveform and quantitative indicators. Furthermore, the algorithm is verified by different clinical signals. The experimental results show that the improved fast ICA algorithm can extract clear fetal heart signals, and there are almost no mixed maternal ECG signals in the extracted FECG signals. The extraction effect of the proposed method is thus optimal than that of the traditional fast ICA method.