Peer-Reviewed Publication
Health Inf Sci Syst2026;14(1):22.December 1, 2026Journal Article
Determination of cardiopulmonary resuscitation quality based on machine learning algorithms using various biological signals.
Byung Jun Kim1, Dong Ah Shin2, Woo Sang Cho1, Soyoon Kwon1, Jung Chan Lee2,3, Taegyun Kim4, Kyung Su Kim4, Gil Joon Suh4,5, Jaehoon Sim6, Jaeheung Park6,7
1Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826 Republic of Korea.
2Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080 Republic of Korea.
3Department of Biomedical Engineering, Seoul National University College of Medicine and Seoul National University Hospital, 103, Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea.
4Department of Emergency Medicine, Seoul National University Hospital, Seoul, 03080 Republic of Korea.
5Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, 03080 Republic of Korea.
6Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826 Republic of Korea.
7Advanced Institute of Convergence Technology (AICT), Suwon, 16229 Korea.
Abstract
PURPOSE: Carotid blood flow (CBF) is a critical indicator during cardiopulmonary resuscitation (CPR), representing blood flow to the brain. In actual clinical settings, measuring it is almost impossible. In this study, we developed and evaluated machine learning models that estimate CBF using biological signals that can be measured during CPR.
METHODS: To simulate various compression conditions i…
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