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使用机器学习算法对冠状动脉微血管功能障碍的基于的可靠非侵入性检测.

Xiaoye Zhao1,2,3, Yinlan Gong4, Lihua Xu5

  • 1School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China.

Mathematical biosciences and engineering : MBE
|July 28, 2023
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概括
此摘要是机器生成的。

使用心电图 (ECG) 数据的机器学习算法可以有效地检测冠状动脉微血管功能障碍 (CMD). 这种非侵入性方法显示出CMD早期患者特异性诊断的前景.

关键词:
冠状动脉微血管功能障碍 (CMD)电心电图 (ECG) 是一种心电图.进入的过程中,机器学习是机器学习.心肌缺血症的心肌缺血症是什么矢量心电图 (VCG) 是指一个心电图.

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科学领域:

  • 心脏病学 心脏病学
  • 生物医学工程 生物医学工程
  • 数据科学数据科学数据科学

背景情况:

  • 冠状动脉微血管功能障碍 (CMD) 是心肌缺血的日益公认的原因.
  • 目前的CMD诊断方法缺乏早期检测的非侵入性能力.

研究的目的:

  • 开发一种机器学习算法,利用心电图 (ECG) 数据进行CMD的非侵入性检测.
  • 建立为患者特异性,早期CMD诊断的基础.

主要方法:

  • 矢量心电图 (VCG) 是从CMD患者和健康对照者的10秒心电图记录中得出的.
  • 对ST-T段进行了多尺度分析 (样本,近似,复杂度指数).
  • 机器学习模型使用顺序向后选择和五倍交叉验证进行了训练和验证.

主要成果:

  • 基于近似 (ApEn) 的支持矢量机 (SVM) 模型表现出最佳性能.
  • 基于ApEn的SVM模型在患者内和患者间的方案中实现了超过0.8的评估指标.
  • 该研究确定了CMD检测中最有效的特征.

结论:

  • 对心电图和VCG信号的值分析可以有效地检测CMD.
  • 开发的机器学习模型为CMD检测提供了一个潜在的非侵入性,基于ECG的工具.
  • 这项研究为早期,针对患者的诊断和CMD的管理铺平了道路.