Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label
Objective: Physicians use electrocardiograms (ECG) to diagnose cardiac abnormalities. Sometimes they need to take a deeper look at abnormal heartbeats to diagnose the patients more precisely. The objective of this research is to design a more accurate heartbeat classification algorithm to assist phy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9869872/ |
| _version_ | 1811211872160972800 |
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| author | Congyu Zou Alexander Muller Utschick Wolfgang Daniel Ruckert Phillip Muller Matthias Becker Alexander Steger Eimo Martens |
| author_facet | Congyu Zou Alexander Muller Utschick Wolfgang Daniel Ruckert Phillip Muller Matthias Becker Alexander Steger Eimo Martens |
| author_sort | Congyu Zou |
| collection | DOAJ |
| description | Objective: Physicians use electrocardiograms (ECG) to diagnose cardiac abnormalities. Sometimes they need to take a deeper look at abnormal heartbeats to diagnose the patients more precisely. The objective of this research is to design a more accurate heartbeat classification algorithm to assist physicians in identifying specific types of the heartbeat. Methods and procedures: In this paper, we propose a novel feature called a segment label, to improve the performance of a heartbeat classifier. This feature, provided by a Convolutional Neural Network, encodes the information surrounding the particular heartbeat. The random forest classifier is trained based on this new feature and other traditional features to classify the heartbeats. Results: We validate our method on the MIT-BIH Arrhythmia dataset following the inter-patient evaluation paradigm. The proposed method is competitive with other similar works. It achieves an accuracy of 0.96, and F1-scores for normal beats, ventricular ectopic beats, and Supra-Ventricular Ectopic Beats (SVEB) of 0.98, 0.93, and 0.74, respectively. The precision and sensitivity for SVEB are 0.76 and 0.78, which outperforms the state-of-the-art methods. Conclusion: This study demonstrates that the segment label can contribute to precisely classifying heartbeats, especially those that require rhythm information as context information (e.g. SVEB). <italic>Clinical impact:</italic> Using a medical devices embedding our algorithm could ease the physicians’ processes of diagnosing cardiovascular diseases, especially for SVEB, in clinical implementation. |
| first_indexed | 2024-04-12T05:20:42Z |
| format | Article |
| id | doaj.art-eaaea3560c6d4de3a5381e0671a59a31 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-04-12T05:20:42Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-eaaea3560c6d4de3a5381e0671a59a312022-12-22T03:46:29ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722022-01-01101810.1109/JTEHM.2022.32027499869872Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment LabelCongyu Zou0https://orcid.org/0000-0002-7076-0553Alexander Muller1Utschick Wolfgang2https://orcid.org/0000-0002-2871-4246Daniel Ruckert3https://orcid.org/0000-0002-5683-5889Phillip Muller4Matthias Becker5Alexander Steger6Eimo Martens7https://orcid.org/0000-0002-5801-0901Klinikum Rechts der Isar der, Technische Universität München, München, GermanyKlinikum Rechts der Isar der, Technische Universität München, München, GermanySignal Processing Group, Technische Universität München, München, GermanyLaboratory for AI in Medicine, Technische Universität München, München, GermanyLaboratory for AI in Medicine, Technische Universität München, München, GermanyFleischhacker GmbH & Company KG, Schwerte, GermanyKlinikum Rechts der Isar der, Technische Universität München, München, GermanyKlinikum Rechts der Isar der, Technische Universität München, München, GermanyObjective: Physicians use electrocardiograms (ECG) to diagnose cardiac abnormalities. Sometimes they need to take a deeper look at abnormal heartbeats to diagnose the patients more precisely. The objective of this research is to design a more accurate heartbeat classification algorithm to assist physicians in identifying specific types of the heartbeat. Methods and procedures: In this paper, we propose a novel feature called a segment label, to improve the performance of a heartbeat classifier. This feature, provided by a Convolutional Neural Network, encodes the information surrounding the particular heartbeat. The random forest classifier is trained based on this new feature and other traditional features to classify the heartbeats. Results: We validate our method on the MIT-BIH Arrhythmia dataset following the inter-patient evaluation paradigm. The proposed method is competitive with other similar works. It achieves an accuracy of 0.96, and F1-scores for normal beats, ventricular ectopic beats, and Supra-Ventricular Ectopic Beats (SVEB) of 0.98, 0.93, and 0.74, respectively. The precision and sensitivity for SVEB are 0.76 and 0.78, which outperforms the state-of-the-art methods. Conclusion: This study demonstrates that the segment label can contribute to precisely classifying heartbeats, especially those that require rhythm information as context information (e.g. SVEB). <italic>Clinical impact:</italic> Using a medical devices embedding our algorithm could ease the physicians’ processes of diagnosing cardiovascular diseases, especially for SVEB, in clinical implementation.https://ieeexplore.ieee.org/document/9869872/Convolutional neural networkECG classificationheartbeat classificationmachine learningmutual information random forest |
| spellingShingle | Congyu Zou Alexander Muller Utschick Wolfgang Daniel Ruckert Phillip Muller Matthias Becker Alexander Steger Eimo Martens Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label IEEE Journal of Translational Engineering in Health and Medicine Convolutional neural network ECG classification heartbeat classification machine learning mutual information random forest |
| title | Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label |
| title_full | Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label |
| title_fullStr | Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label |
| title_full_unstemmed | Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label |
| title_short | Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label |
| title_sort | heartbeat classification by random forest with a novel context feature a segment label |
| topic | Convolutional neural network ECG classification heartbeat classification machine learning mutual information random forest |
| url | https://ieeexplore.ieee.org/document/9869872/ |
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