Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label
Zou C, Mueller A, Wolfgang U, Rueckert D, Mueller P, Becker M, Steger A, Martens E (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 10
Article Number: 1900508
DOI: 10.1109/JTEHM.2022.3202749
Abstract
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). Clinical impact: Using a medical devices embedding our algorithm could ease the physicians' processes of diagnosing cardiovascular diseases, especially for SVEB, in clinical implementation.
Involved external institutions
Germany (DE)How to cite
APA:
Zou, C., Mueller, A., Wolfgang, U., Rueckert, D., Mueller, P., Becker, M.,... Martens, E. (2022). Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label. IEEE Journal of Translational Engineering in Health and Medicine, 10. https://doi.org/10.1109/JTEHM.2022.3202749
MLA:
Zou, Congyu, et al. "Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label." IEEE Journal of Translational Engineering in Health and Medicine 10 (2022).
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