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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

To develop and evaluate deep learning models for cardiac arrest rhythm classification during cardiopulmonary resuscitation (CPR), we analyzed 508 electrocardiogram (ECG) segments (each 4 s in duration, recorded at 250 Hz) from 131 cardiac arrest patients. Compression-affected segments were recorded during chest compressions, while non-compression segments were extracted during compression pauses or immediately after return of spontaneous circulation (ROSC) declaration. One-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) models were employed for four binary classification tasks: (1) shockable rhythms (VF and pVT) versus non-shockable rhythms (asystole and PEA) in all ECG segments; (2) the same classification in compression-affected ECG segments; (3) pulse-generating rhythms (ROSC rhythm) versus non-pulse-generating rhythms (asystole, PEA, VF and pVT) in all ECG segments; and (4) the same classification in compression-affected ECG segments. The 1D-CNN model consistently outperformed the RNN model across all classification tasks. For shockable versus non-shockable rhythm classification, the 1D-CNN achieved accuracies of 91.3% and 89.8% for all ECG segments and compression-affected ECG segments, respectively, compared to 50.6% and 54.5% for the RNN. In detecting pulse-generating rhythms, the 1D-CNN demonstrated accuracies of 90.9% and 85.7% for all ECG segments and compression-affected ECG segments, respectively, while the RNN achieved 92.2% and 84.4%. The 1D-CNN model demonstrated superior performance in cardiac arrest rhythm classification, maintaining high accuracy even with compression-affected ECG data.

Details

Title
A Comparative Study of Convolutional Neural Network and Recurrent Neural Network Models for the Analysis of Cardiac Arrest Rhythms During Cardiopulmonary Resuscitation
Author
Lee, Sijin 1   VIAFID ORCID Logo  ; Lee, Kwang-Sig 2   VIAFID ORCID Logo  ; Park Hyun-Joon 3   VIAFID ORCID Logo  ; Han Kap Su 1   VIAFID ORCID Logo  ; Song Juhyun 1   VIAFID ORCID Logo  ; Lee Sung Woo 1   VIAFID ORCID Logo  ; Kim Su Jin 1   VIAFID ORCID Logo 

 Department of Emergency Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea; [email protected] (S.L.); [email protected] (K.S.H.); [email protected] (J.S.); [email protected] (S.W.L.) 
 AI Center, Korea University College of Medicine, Seoul 02841, Republic of Korea; [email protected] 
 Institute for Healthcare Service Innovation, Korea University College of Medicine, Seoul 08308, Republic of Korea; [email protected] 
First page
4148
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3194490362
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.