Publikace UTB
Repozitář publikační činnosti UTB

A fused electrocardiography arrhythmia detection method

Repozitář DSpace/Manakin

Zobrazit minimální záznam


dc.title A fused electrocardiography arrhythmia detection method en
dc.contributor.author Demiroğlu, Ugur
dc.contributor.author Şenol, Bilal
dc.contributor.author Matušů, Radek
dc.relation.ispartof Multimedia Tools and Applications
dc.identifier.issn 1380-7501 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 1573-7721 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2023
dc.type article
dc.language.iso en
dc.publisher Springer
dc.identifier.doi 10.1007/s11042-023-17410-6
dc.relation.uri https://link.springer.com/article/10.1007/s11042-023-17410-6
dc.relation.uri https://doi.org/10.1007/s11042-023-17410-6
dc.subject ECG en
dc.subject biomedical signal processing and analysis en
dc.subject arrhythmia detection en
dc.subject Hamsi-Pat en
dc.subject PSO en
dc.subject TQWT en
dc.subject INCA feature selection en
dc.subject artificial intelligence en
dc.subject machine learning en
dc.description.abstract Recently, Electrocardiography (ECG) signals are commonly used in diagnosing the cardiac arrhythmia that shows up with the loss of the regular movement of the heart. Approximately 5% of the world population have cardio motor disorders. Therefore, usage of the ECG signals in biomedical signal processing algorithms and machine learning methods for automated diagnosis of this widespread health problem is a popular research topic. In this paper, the Particle Swarm Optimization (PSO) technique is implemented to tune the parameters of Tunable Q-Factor Wavelet Transform (TQWT) and the new generation feature generator Hamsi Hash Function (Hamsi-Pat) is used to obtain the characteristics of the signal. Sub-signals of 10 s obtained from the original ECG signal are divided into their sub-bands of 25 levels with PSO and TQWT. Each of these low pass filters generates 536 dimensional features by applying Hamsi-Pat and statistical methods. Then, all these features are combined and 536 × 25 = 13400-dimensional feature set is obtained. The features in the set are reduced and the best of them are selected by using the Iterative Neighborhood Component Analysis (INCA) method. Finally, the k-Nearest Neighbors (kNN) classification method is applied to the best features according to the City Block measurement criterion. All studies cited to compare the results in this paper also use the MIT-BIH Arrhythmia ECG database. Hence, the difference could be observed in the used techniques. In contrast to the existing studies, this study shows its superior performance by classifying all 17 classes simultaneously by applying a “fused” approach. The method in the paper reached 98.5% classification accuracy on the 17 classes of the MIT-BIH Arrhythmia ECG database. The results indicate that the proposed method showed better rates from the existing studies related to arrhythmia diagnosis using ECG signals in the literature. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011769
utb.identifier.obdid 43885047
utb.identifier.scopus 2-s2.0-85174893083
utb.identifier.wok 001088502300002
utb.identifier.coden MTAPF
utb.source j-scopus
dc.date.accessioned 2024-02-02T10:29:26Z
dc.date.available 2024-02-02T10:29:26Z
utb.ou CEBIA-Tech
utb.contributor.internalauthor Matušů, Radek
utb.fulltext.sponsorship -
utb.wos.affiliation [Demiroglu, Ugur] Fırat Univ, Tech Vocat Sch, Comp Sci Dept, Elazig, Turkiye; [Senol, Bilal] Aksaray Univ, Fac Engn, Software Engn Dept, Aksaray, Turkiye; [Matusu, Radek] Tomas Bata Univ Zlin, Fac Appl Informat, Ctr Secur Informat & Adv Technol CEBIA Tech, Zlin, Czech Republic
utb.scopus.affiliation Computer Sciences Department, Technical Vocational School, Fırat University, Elazığ, Turkey; Software Engineering Department, Faculty of Engineering, Aksaray University, Aksaray, Turkey; Centre for Security, Information and Advanced Technologies (CEBIA–Tech), Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic
utb.fulltext.projects -
Find Full text

Soubory tohoto záznamu

Zobrazit minimální záznam