Determination of body fat percentage by electrocardiography signal with gender based artificial intelligence

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dc.contributor.authorUcar, Muhammed Kursad
dc.contributor.authorUcar, Zeliha
dc.contributor.authorUcar, Kubra
dc.contributor.authorAkman, Mehmet
dc.contributor.authorBozkurt, Mehmet Recep
dc.date.accessioned2024-03-13T10:30:56Z
dc.date.available2024-03-13T10:30:56Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractBackground and purpose: Body fat percentage (BFP) is a frequently used parameter in the assessment of body composition. The body is made up of fat, muscle and lean body tissues. Excess fat tissue in the body causes obesity. Obesity is a treatable disease that decreases the quality of life. Obesity can trigger ailments such as psychological disorders, cardiovascular diseases and respiratory and digestive problems. Dual energy X-ray absorptiometry gold standard method is laborious, costly and time consuming. For this reason, more practical methods are needed. The aim of this study is to develop BFP prediction models with gender-based electrocardiography (ECG) signal and machine learning methods. Methods: In the study, 25 features were extracted from seven different QRS bands and filtered and unfiltered ECG signals. In addition, age, height and weight were used as features. Spearman feature selection algorithm was used to increase the performance. Results: The BFP prediction models developed have performance values of R = 0.94 for men and R = 0.93 for women and R = 0.91 for all individuals. Feature selection algorithm helped increase performance. Conclusionen_US
dc.description.sponsorshipResearch Fund of the Sakarya University [2019-5-19-244]en_US
dc.description.sponsorshipThis work was supported by Research Fund of the Sakarya University. Project Number: 2019-5-19-244en_US
dc.identifier.doi10.1016/j.bspc.2021.102650
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85104667683en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.102650
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3618
dc.identifier.volume68en_US
dc.identifier.wosWOS:000670369200003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectrocardiography signalen_US
dc.subjectMachine learningen_US
dc.subjectArtificial intelligenceen_US
dc.subjectBody compositionen_US
dc.subjectBody fat percentageen_US
dc.subjectGender based body fat percentageen_US
dc.titleDetermination of body fat percentage by electrocardiography signal with gender based artificial intelligenceen_US
dc.typeArticleen_US

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