Academic Journal
Evaluation of customized adaptive cardiovascular activation function with deep neural networks for myocardial infarction classification using ECG image.
| Τίτλος: | Evaluation of customized adaptive cardiovascular activation function with deep neural networks for myocardial infarction classification using ECG image. |
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| Συγγραφείς: | D, Banumathy, Latha Pandala, Madhavi, Masud, Mehedi, Abouhawwash, Mohamed |
| Πηγή: | PeerJ Computer Science; Jun2026, p1-26, 26p |
| Θεματικοί όροι: | Myocardial infarction, Electrocardiography, Mathematical optimization, Random forest algorithms, Artificial neural networks, Statistical accuracy, Subroutines (Computer programs) |
| Περίληψη: | Myocardial infarction (MI), in its early or marginal stages, often presents with subtle and complex electrocardiogram (ECG) patterns that are frequently misclassified or overlooked by conventional diagnostic methods. Traditional ECG analysis techniques rely heavily on handcrafted features, rule-based thresholds, pretrained models, and domain-specific preprocessing, rendering them inadequate for precise, timely myocardial detection in real-time clinical workflows. To address these challenges, this research proposes a robust deep learning-based model with a customized activation function for the automatic classification of MI ECG signals, with a primary focus on accurate identification of myocardial cases, along with Normal and other Abnormal cases. The model combines a ResNet-50 architecture fused with a customised activation function for improved feature extraction. This model is integrated with a Bayesian-optimised Random Forest classifier to ensure accurate and generalised classification. The proposed model achieves an accuracy of 99.9%, precision of 99%, recall of 99%, and specificity of 98% by surpassing the performance of adaptive cardiovascular activation function (ACAF)-based Residual Network (ResNet) and other baseline methods. Comparative interpretation with existing models confirms its higher performance and reliability. These results indicate that the proposed approach can serve as a potent, deployable diagnostic system for automated detection of early myocardial infarction. [ABSTRACT FROM AUTHOR] |
| Copyright of PeerJ Computer Science is the property of PeerJ Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 195336239 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluation of customized adaptive cardiovascular activation function with deep neural networks for myocardial infarction classification using ECG image. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22D%2C+Banumathy%22">D, Banumathy</searchLink><br /><searchLink fieldCode="AR" term="%22Latha+Pandala%2C+Madhavi%22">Latha Pandala, Madhavi</searchLink><br /><searchLink fieldCode="AR" term="%22Masud%2C+Mehedi%22">Masud, Mehedi</searchLink><br /><searchLink fieldCode="AR" term="%22Abouhawwash%2C+Mohamed%22">Abouhawwash, Mohamed</searchLink> – Name: TitleSource Label: Source Group: Src Data: PeerJ Computer Science; Jun2026, p1-26, 26p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Myocardial+infarction%22">Myocardial infarction</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Subroutines+%28Computer+programs%29%22">Subroutines (Computer programs)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Myocardial infarction (MI), in its early or marginal stages, often presents with subtle and complex electrocardiogram (ECG) patterns that are frequently misclassified or overlooked by conventional diagnostic methods. Traditional ECG analysis techniques rely heavily on handcrafted features, rule-based thresholds, pretrained models, and domain-specific preprocessing, rendering them inadequate for precise, timely myocardial detection in real-time clinical workflows. To address these challenges, this research proposes a robust deep learning-based model with a customized activation function for the automatic classification of MI ECG signals, with a primary focus on accurate identification of myocardial cases, along with Normal and other Abnormal cases. The model combines a ResNet-50 architecture fused with a customised activation function for improved feature extraction. This model is integrated with a Bayesian-optimised Random Forest classifier to ensure accurate and generalised classification. The proposed model achieves an accuracy of 99.9%, precision of 99%, recall of 99%, and specificity of 98% by surpassing the performance of adaptive cardiovascular activation function (ACAF)-based Residual Network (ResNet) and other baseline methods. Comparative interpretation with existing models confirms its higher performance and reliability. These results indicate that the proposed approach can serve as a potent, deployable diagnostic system for automated detection of early myocardial infarction. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PeerJ Computer Science is the property of PeerJ Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7717/peerj-cs.3539 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Myocardial infarction Type: general – SubjectFull: Electrocardiography Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Statistical accuracy Type: general – SubjectFull: Subroutines (Computer programs) Type: general Titles: – TitleFull: Evaluation of customized adaptive cardiovascular activation function with deep neural networks for myocardial infarction classification using ECG image. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: D, Banumathy – PersonEntity: Name: NameFull: Latha Pandala, Madhavi – PersonEntity: Name: NameFull: Masud, Mehedi – PersonEntity: Name: NameFull: Abouhawwash, Mohamed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23765992 Titles: – TitleFull: PeerJ Computer Science Type: main |
| ResultId | 1 |