short-paper

Efficient Data Augmentation Policy for Electrocardiograms

Authors:

Byeong Tak Lee,

Joon-myoung KwonAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 4153 - 4157

https://doi.org/10.1145/3511808.3557591

Published: 17 October 2022 Publication History

Abstract

We present the taxonomy of data augmentation for electrocardiogram (ECG) after reviewing various ECG augmentation methods. On the basis of the taxonomy, we demonstrate the effect of augmentation methods on the ECG classification via extensive experiments. Initially, we examine the performance trend as the magnitude of distortion increases and identify the optimal distortion magnitude. Secondly, we investigate the synergistic combinations of the transformations and identify the pairs of transformations with the greatest positive effect. Finally, based on our experimental findings, we propose an efficient augmentation policy and demonstrate that it outperforms previous augmentation policies.

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Digital Library

Cited By

Lee HHan GKim KKang SJang JJo YSon JLee MKwon JOh B(2025)Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patientsInternational Journal of Medical Informatics10.1016/j.ijmedinf.2025.105843197(105843)Online publication date: May-2025
https://doi.org/10.1016/j.ijmedinf.2025.105843
Haro PHevia-Montiel NPerez-Gonzalez J(2023)ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine ModelTropical Medicine and Infectious Disease10.3390/tropicalmed80301578:3(157)Online publication date: 4-Mar-2023
https://doi.org/10.3390/tropicalmed8030157

Index Terms

Efficient Data Augmentation Policy for Electrocardiograms
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 17 October 2022

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Ministry of Health and Wel- fare of South Korea and Korea Health Information Service and National Research Foundation of Korea

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CIKM '22

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Lee HHan GKim KKang SJang JJo YSon JLee MKwon JOh B(2025)Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patientsInternational Journal of Medical Informatics10.1016/j.ijmedinf.2025.105843197(105843)Online publication date: May-2025
https://doi.org/10.1016/j.ijmedinf.2025.105843
Haro PHevia-Montiel NPerez-Gonzalez J(2023)ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine ModelTropical Medicine and Infectious Disease10.3390/tropicalmed80301578:3(157)Online publication date: 4-Mar-2023
https://doi.org/10.3390/tropicalmed8030157

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