A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm

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dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.contributor.authorKiymac, Evren
dc.contributor.authorKaya, Yasin
dc.date.accessioned2025-01-06T17:36:26Z
dc.date.available2025-01-06T17:36:26Z
dc.date.issued2023
dc.description.abstractCardiac arrhythmias indicate cardiovascular disease which is the leading cause of mortality worldwide, and can be detected by an electrocardiogram (ECG). Automated deep learning methods have been developed to overcome the disadvantages of manual interpretation by medical experts. The performance of the networks strongly depends on hyperparameter optimization (HPO), and this NP-hard problem is suitable for metaheuris-tic (MH) methods. In this study, a novel method is proposed for the HPO of a convolutional neural network (CNN) arrhythmia classifier using an MH algorithm. The approach utilizes our variant of an MH method, named the memory-enhanced artificial hummingbird algorithm, which has an additional memory unit that stores the evaluations of the solutions and reduces the computation time significantly. The study also proposes a novel fitness function that considers both the accuracy rate and the total number of parameters of each candidate network. Experiments were conducted on raw ECG samples from the MIT-BIH arrhythmia database. The proposed method was compared with five other MH methods and achieved equal or outperforming results, with classification accuracy reaching 98.87%. The proposed method yielded promising results in finding a high-performing solution with relatively lower complexity.
dc.identifier.doi10.1016/j.eswa.2022.119162
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85141524540
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.119162
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1877
dc.identifier.volume213
dc.identifier.wosWOS:000891788100007
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArrhythmia classification
dc.subjectECG
dc.subjectDeep learning
dc.subjectCNN
dc.subjectHyperparameter optimization
dc.subjectArtificial hummingbird algorithm
dc.titleA novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm
dc.typeArticle

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