Analysis on population-based algorithm optimized filter for non-invasive fECG extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254651" target="_blank" >RIV/61989100:27240/23:10254651 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1568494623003411?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494623003411?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2023.110323" target="_blank" >10.1016/j.asoc.2023.110323</a>

Result language

angličtina

Original language name

Analysis on population-based algorithm optimized filter for non-invasive fECG extraction

Original language description

Metaheuristic algorithms (MAs) have become one of the primary tools for optimization in diverse domains, including non-invasive fetal electrocardiogram (fECG) extraction. Research reveals that hyperparameters affect the performance of MAs differently to problems, and some algorithms are problem-specific designed and may produce bad results for different problems. Hence, three questions arise, (1) how much can we trust MAs when solving boxed-constrained fECG extraction problems, and (2) which type of MAs are suitable and adequate for fECG extraction? (3) do MAs find acceptable solutions to a problem that does not formulate the problem comprehensively with an imperfect objective function? This paper focuses on these three inquiries and proposes a framework providing an MA-assisted adaptive filter for non-invasive fECG extraction. The proposed framework has three key components: data pre-processing, MA-based adaptive filter, and post-processing. The pre-processing starts to process the signals detected by pregnant women and extract the desired signal by independent component analysis. Such output signals are then passed to a filter and optimized by population -based algorithms, producing an optimal solution. After that, this solution, as filter weights, will be used for signal extraction and be processed by post-processing. Importantly, the proposed framework is user-friendly that can import any MA algorithm to run and disassemble as a separate assisting tool. This work investigated eight classic and disparate MA algorithms on the Abdominal and Direct Fetal ECG Database (ADFECGDB) dataset, offering comprehensive experimental analysis. We infer from the results that the recordings r01, r02, r03, r05, r08, and r09, where the signals are less noisy and can be solved well. While for recordings r04, r07, r06, and r10, the performance can be influenced by hyperparameters for any test algorithms, such as the population size, window size, and other parameters in MAs. Meanwhile, we found that MA may not be the primary target influencing the performance vary. &amp; COPY; 2023 Elsevier B.V. All rights reserved.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

<a href="/en/project/LTAIN19176" target="_blank" >LTAIN19176: Metaheuristics Framework for Multi-objective Combinatorial Optimization Problems (META MO-COP)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

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

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

142

Issue of the periodical within the volume

Jul 2023

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

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UT code for WoS article

001052889400001

EID of the result in the Scopus database

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