A novel Discrete Artificial Bee Colony algorithm combined with adaptive filtering to extract Fetal Electrocardiogram signals.

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators. • A new objective function is designed to combine heuristic algorithms and LMS filter to extract the FECG signal from AECG signal. • This paper propose a novel Discrete Artificial Bee Colony, which introduce a tailored initialization strategy to improve the algorithm's convergence speed, based on the specific characteristics of the FECG signal extraction problem. The novel algorithm can obtain clearer extracted signal than other existed heuristic algorithms. • We incorporate the strategies of random wavelet basic functions and Gaussian distribution in the DABC algorithm to enhance the global search ability and prevent the population from prematurely becoming trapped in a local optimum. [ABSTRACT FROM AUTHOR]