MnL–TWA: Manifold Learning Approach for T–Wave Alternans Detection in Ambulatory Environments.
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| Title: | MnL–TWA: Manifold Learning Approach for T–Wave Alternans Detection in Ambulatory Environments. |
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| Authors: | Pascual–Sánchez, Lidia1 (AUTHOR) lidia.pascual@uah.es, Goya–Esteban, Rebeca2 (AUTHOR), Blanco–Velasco, Manuel1 (AUTHOR) |
| Source: | Biomedical Engineering & Computational Biology. 7/2/2026, Vol. 17, p1-18. 18p. |
| Subjects: | Dimensional reduction algorithms, Machine learning, Electrocardiography, Feature extraction, Artificial neural networks, Wearable technology |
| Abstract: | Background: T-wave alternans (TWA) refers to variations in the ventricular repolarization pattern observed on the ECG, which has been associated with cardiac instability and an increased risk of sudden cardiac death. Recently, machine learning (ML) methods have been developed for TWA detection, but their black-box nature limits interpretability. Objectives: To address this gap, we propose manifold learning (MnL) to enhance the explainability of these learning models while maintaining TWA detection effectiveness. Methods: We fine-tuned nonlinear dimension reduction techniques such as Uniform Manifold Approximation and Projection (UMAP), Isometric Mapping (Isomap), and autoencoders (AE) in combination with ML methods, namely K-nearest neighbors (KNN), random forest (RF), and neural networks (NN). Performance was evaluated using mean and standard deviation across patient-wise permutations. Results: In the design stage, the AE-based NN effectively retained essential discriminative information (F1-score 92.1 ± 2.4 %). MnL-generated spaces consistently revealed that misclassifications primarily lie close to the decision boundary and are predominantly associated with lower TWA voltages, which are more dispersed within the space. For ambulatory TWA detection, Isomap combined with RF and the AE-based NN achieved performance comparable to using the complete set of features derived from established TWA analysis methods (F1-score 78.5 ± 6.4 % and 77.9 ± 5.4 %, respectively), including spectral, time-domain, and correlation-based descriptors. The latent space visualization shows that predictions that ultimately become detections are located farther away from the decision boundary. Conclusion: MnL-generated spaces provide valuable insights into how classification models differentiate between TWA and non-TWA instances, as well as the patterns in TWA event amplitudes. This approach helps bridge the gap between performance and transparency, supporting more clinically reliable TWA detection. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomedical Engineering & Computational Biology is the property of Sage Publications 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 195095187 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MnL–TWA: Manifold Learning Approach for T–Wave Alternans Detection in Ambulatory Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pascual–Sánchez%2C+Lidia%22">Pascual–Sánchez, Lidia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lidia.pascual@uah.es</i><br /><searchLink fieldCode="AR" term="%22Goya–Esteban%2C+Rebeca%22">Goya–Esteban, Rebeca</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Blanco–Velasco%2C+Manuel%22">Blanco–Velasco, Manuel</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biomedical+Engineering+%26+Computational+Biology%22">Biomedical Engineering & Computational Biology</searchLink>. 7/2/2026, Vol. 17, p1-18. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: T-wave alternans (TWA) refers to variations in the ventricular repolarization pattern observed on the ECG, which has been associated with cardiac instability and an increased risk of sudden cardiac death. Recently, machine learning (ML) methods have been developed for TWA detection, but their black-box nature limits interpretability. Objectives: To address this gap, we propose manifold learning (MnL) to enhance the explainability of these learning models while maintaining TWA detection effectiveness. Methods: We fine-tuned nonlinear dimension reduction techniques such as Uniform Manifold Approximation and Projection (UMAP), Isometric Mapping (Isomap), and autoencoders (AE) in combination with ML methods, namely K-nearest neighbors (KNN), random forest (RF), and neural networks (NN). Performance was evaluated using mean and standard deviation across patient-wise permutations. Results: In the design stage, the AE-based NN effectively retained essential discriminative information (F1-score 92.1 ± 2.4 %). MnL-generated spaces consistently revealed that misclassifications primarily lie close to the decision boundary and are predominantly associated with lower TWA voltages, which are more dispersed within the space. For ambulatory TWA detection, Isomap combined with RF and the AE-based NN achieved performance comparable to using the complete set of features derived from established TWA analysis methods (F1-score 78.5 ± 6.4 % and 77.9 ± 5.4 %, respectively), including spectral, time-domain, and correlation-based descriptors. The latent space visualization shows that predictions that ultimately become detections are located farther away from the decision boundary. Conclusion: MnL-generated spaces provide valuable insights into how classification models differentiate between TWA and non-TWA instances, as well as the patterns in TWA event amplitudes. This approach helps bridge the gap between performance and transparency, supporting more clinically reliable TWA detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Biomedical Engineering & Computational Biology is the property of Sage Publications 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: 10.1177/11795972261463531 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Dimensional reduction algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Electrocardiography Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Wearable technology Type: general Titles: – TitleFull: MnL–TWA: Manifold Learning Approach for T–Wave Alternans Detection in Ambulatory Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pascual–Sánchez, Lidia – PersonEntity: Name: NameFull: Goya–Esteban, Rebeca – PersonEntity: Name: NameFull: Blanco–Velasco, Manuel IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 07 Text: 7/2/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11795972 Numbering: – Type: volume Value: 17 Titles: – TitleFull: Biomedical Engineering & Computational Biology Type: main |
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