A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System

被引：0

作者：

Kim, Byung Jun ^{[1
]}

Shin, Dong Ah ^{[1
]}

Sim, Jaehoon ^{[1
,2
]}

Cho, Woo Sang ^{[1
]}

Kwon, So Yoon ^{[1
]}

Suh, Gil Joon ^{[1
,3
]}

Kim, Kyung Su ^{[1
,3
]}

Kim, Taegyun ^{[1
,3
]}

Lee, Jung Chan ^{[1
,3
,4
]}

机构：

[1] Seoul Natl Univ, Seoul, South Korea

[2] Bluerobin Inc, Seoul, South Korea

[3] Seoul Natl Univ Hosp, Seoul, South Korea

[4] Seoul Natl Univ, Med Res Ctr, Seoul, South Korea

来源：

INTELLIGENT AUTONOMOUS SYSTEMS 18, VOL 1, IAS18-2023 | 2024年 / 795卷

基金：

新加坡国家研究基金会;

关键词：

Carotid blood volume; Classification; Machine learning; Bio signals; HEART-ASSOCIATION GUIDELINES; PERFUSION-PRESSURE;

D O I：

10.1007/978-3-031-44851-5_26

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

During Cardio Pulmonary Resuscitation (CPR), appropriate heart compression affects the quality of CPR, which is directly related to the patient's life. Therefore, it is important to accurately judge the quality of CPR. Therefore, it is important to accurately judge the quality of CPR. Until now, there have been studies on bio signal-based CPR feedback systems such as EtCO2 (End tidal CO2, EtCO2), Photoplethysmography (PPG). However, it is not possible to provide an accurate basis for improvement in compression. Therefore, in this study, a machine learning-based CBV (Carotid Blood Volume) classification model was developed for various bio-signal data. In the results, Sensitivity, Specificity, Precision, and Accuracy had values of 0.91, 0.97, 0.94, and 0.95, respectively, and showed high classification performance. Therefore, the CBV classification model presented in this study will be able to become a model based on a feedback system that can intuitively judge the quality of current CPR.

引用

页码：345 / 351

页数：7

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