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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257039" target="_blank" >RIV/61989100:27240/24:10257039 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417424000381?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417424000381?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2024.123173" target="_blank" >10.1016/j.eswa.2024.123173</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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. © 2024 Elsevier Ltd

Název v anglickém jazyce

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

Popis výsledku anglicky

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. © 2024 Elsevier Ltd

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

—

Svazek periodika

247

Číslo periodika v rámci svazku

Aug

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

—

Kód UT WoS článku

001173710600001

EID výsledku v databázi Scopus

2-s2.0-85184054626