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使用深度神经网络进行端到端的早发性心室收缩检测.

Dimitri Kraft1, Gerald Bieber1, Peter Jokisch2

  • 1Fraunhofer IGD Rostock, 18059 Rostock, Germany.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
概括

一个新的1D U-Net模型在霍尔特监测期间检测QRS复合体时显示出高精度. 虽然对于一般的心律分析有效,但对于精确检测心室过早收缩 (PVCs) 需要进一步改进.

科学领域:

  • 心脏病学 心脏病学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 在霍尔特监测中,准确的心律评估依赖于精确识别心跳和心室过早收缩 (PVC).
  • 传统的霍尔特分析方法在可靠检测复杂心律失常时面临挑战.

研究的目的:

  • 引入和评估一种新的1D U-Net神经网络,用于在霍尔特记录中更好地检测PVC.
  • 评估模型在QRS复杂检测和PVC识别中的表现,并根据已建立的方法对其进行评估.

主要方法:

  • 使用Icentia 11k,INCART DB和一个定制数据集来训练1D U-Net模型.
  • 在AHA DB,MIT 11 DB,NST和定制的真实世界数据集上验证了模型,将结果与传统的霍尔特分析进行比较.

主要成果:

  • 1D U-Net模型在所有测试的数据库中实现了QRS复杂检测的近乎完美的平衡精度.
  • 对PVC检测的平衡精度在0.909到0.986之间,尽管存在一些变化,但表现强.
  • 虽然灵敏度有所不同,但模型的平衡精度表明在识别假阳性和假阴性时的表现均等.

结论:

  • 1D U-Net 架构对于 QRS 复杂检测在 Holter 监控中非常有效.
关键词:
1D U-Net 神经网络的神经网络霍尔特监控的监控方法发现心室过早收缩 (PVC) 的检测.

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  • 需要进一步的研究和模型改进,以提高PVC检测的准确性,解决现实世界的复杂性和噪音.