ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects-Reference-Cited by-同舟云学术

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ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects

Published:2022-07 Issue: Volume:146 Page:105540
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Agrawal Amulya,Chauhan Aniket,Shetty Manu Kumar,P Girish M.,Gupta Mohit D.,Gupta Anubha^ORCID

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference45 articles.

1. Comparison of rapid antigen tests for COVID-19;Yamayoshi;Viruses,2020

2. Generalized SIR (GSIR) epidemic model: an improved framework for the predictive monitoring of COVID-19 pandemic;Singh;ISA Trans.,2021

3. An improved data driven dynamic SIRD model for predictive monitoring of COVID-19;Singh,2021

4. Recent developments on therapeutic and diagnostic approaches for COVID-19;Majumder;AAPS J.,2021

5. Post-COVID-19 heart syndrome;Gasecka;Cardiol. J.,2021

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1. Post-COVID effect on heart after recovery based on hybrid EfficientNet-DBN with multilevel classification using ECG images;EngMedicine;2024-09

2. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions;Diagnostics;2024-08-23

3. A review of evaluation approaches for explainable AI with applications in cardiology;Artificial Intelligence Review;2024-08-09

4. Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods;Frontiers in Cardiovascular Medicine;2024-06-25

5. Exploring Interpretable AI Methods for ECG Data Classification;The Fifth Workshop on Intelligent Cross-Data Analysis and Retrieval;2024-06-10

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