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基于深度学习的ROSC预测和心电图表征在医院外心脏骤停中的ECG表型

Dong Hyun Choi¹, Ki Jeong Hong¹, Ki Hong Kim¹

¹Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea; Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea; Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea.

|March 7, 2026

概括

一个新的卷积神经网络 (CNN) 模型准确地预测自发循环的恢复 (ROSC) 并在非医院心脏骤停 (OHCA) 患者中识别新的心电图 (ECG) 现型,改善复苏结果.

科学领域:

人工智能在医学中的应用
心脏病学心脏病学
紧急医疗紧急医疗

背景情况:

心脏骤停中的心电图 (ECG) 信号提供了对心律和复苏成功的洞察力.
预测自发循环的恢复 (ROSC) 和了解医院外心脏骤停 (OHCA) 的心电图特征对于改善患者的治疗结果至关重要.

研究的目的:

开发一个卷积神经网络 (CNN) 模型,用于预测OHCA患者的ROSC.
识别与不同ROSC概率和节奏相关的新型ECG表型.

主要方法:

一项回顾性队列研究分析了3,452名成年OHCA患者的ECG数据.
开发了一种1D CNN,可以在2分钟内预测ROSC,并对心电图节奏进行分类.
K-means集群被用来从CNN特征表示中识别ECG表型.

主要成果:

在预测ROSC (AUC 0.921) 和可震动节奏 (AUC 0.983) 方面,CNN模型取得了高准确性.
确定了五种不同的心电图表现型,每个都与不同的ROSC概率有关.
现象类型从高ROSC (30.4%) 的可震动节奏到低ROSC的 asystole.

结论:

一个基于CNN的模型准确地预测了来自OHCA心电图的ROSC和可震动的节奏.
确定了新的心电图表现型,为个性化的医院前复苏策略提供了潜力.
这种方法可以增强临床决策和改善OHCA患者的治疗结果.

关键词:

电心电图 (ECG) 是一种心电图.个性化治疗个性化治疗在医院外发生心脏骤停.现象类型现象类型复苏是复苏的方法之一.恢复了自发的循环.

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