A novel efficient hybrid deep learning framework for ECG-based heartbeat arrhythmia classification.
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| Title: | A novel efficient hybrid deep learning framework for ECG-based heartbeat arrhythmia classification. |
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| Authors: | Bahrami, Reza1, elec97.bahrami@tafreshu.ac.ir, Fotouhi, Ali M.1, fotouhi@tafreshu.ac.ir |
| Source: | Neural Computing & Applications; Jul2025, Vol. 37 Issue 21, p16409-16425, 17p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 186779385 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel efficient hybrid deep learning framework for ECG-based heartbeat arrhythmia classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Bahrami%2C+Reza%22">Bahrami, Reza</searchLink><relatesTo>1</relatesTo>, <i>elec97.bahrami@tafreshu.ac.ir</i><br /><searchLink fieldCode="AU" term="%22Fotouhi%2C+Ali+M%2E%22">Fotouhi, Ali M.</searchLink><relatesTo>1</relatesTo>, <i>fotouhi@tafreshu.ac.ir</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>; Jul2025, Vol. 37 Issue 21, p16409-16425, 17p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=186779385 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-025-11319-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 16409 Titles: – TitleFull: A novel efficient hybrid deep learning framework for ECG-based heartbeat arrhythmia classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bahrami, Reza – PersonEntity: Name: NameFull: Fotouhi, Ali M. IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 21 Titles: – TitleFull: Neural Computing & Applications Type: main |
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