Machine Learning Models to Predict Cardiovascular Events from Heart Rate Variability Data-Reference-Cited by-同舟云学术

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Machine Learning Models to Predict Cardiovascular Events from Heart Rate Variability Data

Published:2022-10-26 Issue: Volume: Page:
ISSN:
Container-title:2022 3rd International Conference on Human-Centric Smart Environments for Health and Well-being (IHSH)
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Author:

Moshawrab Mohammad¹,Adda Mehdi¹,Bouzouane Abdenour²,Ibrahim Hussein³,Raad Ali⁴

Affiliation:

1. Université du Québec à Rimouski,Département de Mathématiques, Informatique et génie,Rimouski,Canada

2. Université du Québec à Chicoutimi,Département D’informatique et de Mathématique,Chicoutimi,Canada

3. Institut Technologique de Maintenance Industrielle,Sept-Îles,Canada

4. Islamic University of Lebanon,Dean of Faculty of Arts & Sciences,Wardaniyeh,Lebanon

Funder

Natural Sciences and Engineering Research Council of Canada

Fonds Québécois de la Recherche sur la Nature et les Technologies

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10091920/10091993/10092060.pdf?arnumber=10092060

Reference35 articles.

1. A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals

2. Cross-Validation

3. Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction

4. Tab-transformer: Tabular data modeling using contextual embeddings;huang,2020

5. MIT-BIH Normal Sinus Rhythm Database v1.0.0;PhysioNet,1999

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