Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label-Reference-Cited by-同舟云学术

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Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label

Published:2022 Issue: Volume:10 Page:1-8
ISSN:2168-2372
Container-title:IEEE Journal of Translational Engineering in Health and Medicine
language:
Short-container-title:IEEE J. Transl. Eng. Health Med.

Author:

Zou Congyu¹^ORCID,Muller Alexander¹,Wolfgang Utschick²^ORCID,Ruckert Daniel³^ORCID,Muller Phillip³,Becker Matthias⁴,Steger Alexander¹,Martens Eimo¹^ORCID

Affiliation:

1. Klinikum Rechts der Isar der, Technische Universität München, München, Germany

2. Signal Processing Group, Technische Universität München, München, Germany

3. Laboratory for AI in Medicine, Technische Universität München, München, Germany

4. Fleischhacker GmbH & Company KG, Schwerte, Germany

Funder

Fleischhacker GmbH & Company KG

Klinikum Rechts der Isar, Technische Universität München

Technische Universität München

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Medicine

Link

http://xplorestaging.ieee.org/ielx7/6221039/9652399/09869872.pdf?arnumber=9869872

Reference36 articles.

1. Electrocardiogram Classification by Modified EfficientNet with Data Augmentation

2. Heartbeat classification using projected and dynamic features of ECG signal

3. Scikit-learn: Machine learning in Python;pedregosa;J Mach Learn Res,2012

4. Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks

5. A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification

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