Combining Ventricular Fibrillation Features With Defibrillation Waveform Parameters Improves the Ability to Predict Shock Outcomes in a Rabbit Model of Cardiac Arrest

In this animal model, combining VF features with defibrillation parameters greatly enhanced the ability of shock outcome prediction, whether it was based on extracted features/parameters or directly using raw waveforms with machine learning methods.

Authors

Yushun Gong
Jianjie Wang (ORCID: https://orcid.org/0000-0003-0983-1065)
Liang Wei
Yongqin Li (ORCID: https://orcid.org/0000-0002-6114-2382)
Jingru Li (ORCID: https://orcid.org/0000-0003-0502-686X)

Institutions

Army Medical University (CN)
Army Medical College (PK)

Publication Details

Journal: Journal of the American Heart Association
Published: 2025-03-27
DOI: https://doi.org/10.1161/jaha.124.039527
Primary Topic: Cardiac Arrest and Resuscitation
Type: article
Field-Weighted Citation Impact: 0.00

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article

Combining Ventricular Fibrillation Features With Defibrillation Waveform Parameters Improves the Ability to Predict Shock Outcomes in a Rabbit Model of Cardiac Arrest

Yushun Gong, Jianjie Wang, Liang Wei, Yongqin Li et al.

Mar 27, 2025

Journal of the American Heart Association

Cardiac Arrest and Resuscitation

article

Combining Ventricular Fibrillation Features With Defibrillation Waveform Parameters Improves the Ability to Predict Shock Outcomes in a Rabbit Model of Cardiac Arrest

Yushun Gong, Jianjie Wang, Liang Wei, Yongqin Li, Jingru Li

March 27, 2025article en

Abstract

In this animal model, combining VF features with defibrillation parameters greatly enhanced the ability of shock outcome prediction, whether it was based on extracted features/parameters or directly using raw waveforms with machine learning methods.

Journal of the American Heart AssociationVol. 14(7)

Army Medical University (CN), Army Medical College (PK)

Openalex Percentile: Top 90%

Cardiac Arrest and Resuscitation

DOI Open Access PDF OpenAlex

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