Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations

Tamás Dózsa; Gergö Bognar; Peter Kovacs

doi:10.1007/978-3-030-45096-0_44

Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations

Tamás Dózsa
, Gergö Bognar
, Peter Kovacs

Institute of Signal Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In this work, we are focusing on the problem of heartbeat classification in electrocardiogram (ECG) signals. First we develop a patient-specific feature extraction scheme by using adaptive orthogonal transformations based on wavelets, B-splines, Hermite and rational functions. The so-called variable projection provides the general framework to find the optimal nonlinear parameters of these transformations. After extracting the features, we train a support vector machine (SVM) for each model whose outputs are combined via ensemble learning techniques. In the experiments, we achieved an accuracy of 94.2% on the PhysioNet MIT-BIH Arrhythmia Database that shows the potential of the proposed signal models in arrhythmia detection.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	355-363
Number of pages	9
ISBN (Electronic)	978-3-030-45096-0
ISBN (Print)	9783030450953
DOIs	https://doi.org/10.1007/978-3-030-45096-0_44
Publication status	Published - Apr 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12014 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Fields of science

102019 Machine learning
202 Electrical Engineering, Electronics, Information Engineering
202022 Information technology
202037 Signal processing

JKU Focus areas

Digital Transformation

Access to Document

10.1007/978-3-030-45096-0_44

Cite this

Dózsa, T., Bognar, G., & Kovacs, P. (2020). Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations. In R. Moreno-Díaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers (pp. 355-363). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12014 LNCS). Springer. https://doi.org/10.1007/978-3-030-45096-0_44

Dózsa, Tamás ; Bognar, Gergö ; Kovacs, Peter. / Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations. Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. editor / Roberto Moreno-Díaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2020. pp. 355-363 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations",

abstract = "In this work, we are focusing on the problem of heartbeat classification in electrocardiogram (ECG) signals. First we develop a patient-specific feature extraction scheme by using adaptive orthogonal transformations based on wavelets, B-splines, Hermite and rational functions. The so-called variable projection provides the general framework to find the optimal nonlinear parameters of these transformations. After extracting the features, we train a support vector machine (SVM) for each model whose outputs are combined via ensemble learning techniques. In the experiments, we achieved an accuracy of 94.2\% on the PhysioNet MIT-BIH Arrhythmia Database that shows the potential of the proposed signal models in arrhythmia detection.",

author = "Tam{\'a}s D{\'o}zsa and Gerg{\"o} Bognar and Peter Kovacs",

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Dózsa, T, Bognar, G & Kovacs, P 2020, Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations. in R Moreno-Díaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12014 LNCS, Springer, pp. 355-363. https://doi.org/10.1007/978-3-030-45096-0_44

Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations. / Dózsa, Tamás; Bognar, Gergö; Kovacs, Peter.
Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Díaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2020. p. 355-363 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12014 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations

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AU - Bognar, Gergö

AU - Kovacs, Peter

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N2 - In this work, we are focusing on the problem of heartbeat classification in electrocardiogram (ECG) signals. First we develop a patient-specific feature extraction scheme by using adaptive orthogonal transformations based on wavelets, B-splines, Hermite and rational functions. The so-called variable projection provides the general framework to find the optimal nonlinear parameters of these transformations. After extracting the features, we train a support vector machine (SVM) for each model whose outputs are combined via ensemble learning techniques. In the experiments, we achieved an accuracy of 94.2% on the PhysioNet MIT-BIH Arrhythmia Database that shows the potential of the proposed signal models in arrhythmia detection.

AB - In this work, we are focusing on the problem of heartbeat classification in electrocardiogram (ECG) signals. First we develop a patient-specific feature extraction scheme by using adaptive orthogonal transformations based on wavelets, B-splines, Hermite and rational functions. The so-called variable projection provides the general framework to find the optimal nonlinear parameters of these transformations. After extracting the features, we train a support vector machine (SVM) for each model whose outputs are combined via ensemble learning techniques. In the experiments, we achieved an accuracy of 94.2% on the PhysioNet MIT-BIH Arrhythmia Database that shows the potential of the proposed signal models in arrhythmia detection.

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Dózsa T, Bognar G, Kovacs P. Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations. In Moreno-Díaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Springer. 2020. p. 355-363. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-45096-0_44

Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

Morphology based Cardiac Signal Processing

Cite this

Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

Morphology based Cardiac Signal Processing

Cite this