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基于深度学习的卷积神经网络模型对实时腹腔动脉高心率警报分类的作用

Unmesh Khanolkar¹, Ashish Yadav¹, Avdhesh Mann^2,3

¹Division of Cardiac Electrophysiology, University of Toledo, Toledo, Ohio, USA.

Journal of cardiovascular electrophysiology

|January 27, 2026

概括

一个新的深度学习模型准确地检测了重症监护室 (ICU) 中的腹腔动脉冲动脉 (VT) 报警,显著减少了错误的阳性结果,并减轻了医疗保健提供者的报警疲劳.

科学领域:

关键护理医学关键护理医学
生物医学工程生物医学工程
医疗保健中的人工智能

背景情况:

腹腔动脉短拍 (VT) 是一种危险的心律,需要在重症监护室 (ICU) 及时检测.
当前的监控系统产生了高率的假阳性报警,导致医疗保健专业人员的报警疲劳.
报警疲劳会导致无敏化,反应延迟,并增加临床医生的认知负载.

研究的目的:

开发和评估一个深度学习模型,用于准确分类VT报警.
为了减少在持续的心脏监测中出现假阳性报警的发生率.
为了减轻ICU环境中的警报疲劳.

主要方法:

开发了一个基于深度学习的,一维卷积神经网络 (1D-CNN).
该模型利用了多个原始波形输入:两个心电图 (ECG) 导线,光电脉冲图 (PPG) 和动脉血压 (ABP) 信号.
在VTaC心律失常基准数据集上进行训练,使用VT报警之前的10秒预处理的波形段进行训练.

主要成果:

在1D-CNN模型的测试中,在测试组中,接收器运行特征曲线 (AUC) 下的面积达到0.901.
性能指标包括整体准确度为83.22%,F1得分为73.3%,灵敏度为77.53%,特异性为85.63%.
该模型成功检测到超过75%的VT警报,同时大幅降低了假阳性率.

结论:

深度学习,特别是1D-CNN模型,可以有效地区分真实和虚假的VT报警.
使用短段的原始波形数据可以实现强大的VT报警分类.
这种方法有望提高ICU心脏监测系统的可靠性.

关键词:

警报疲劳警报疲劳卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.腹腔动脉短心症是什么意思

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