心率的最佳长度心率变化数据和预测时间用于用机器学习预测心室动
Da Un Jeong1, Getu Tadele Taye2, Han-Jeong Hwang3
1Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Computational and mathematical methods in medicine
|August 21, 2023
概括
使用心率变化 (HRV) 预测心室动 (VF) 对于预防心脏突然死亡至关重要. 这项研究发现,VF发作附近的HRV特征通过人工神经网络实现了高预测准确度.
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:
- 心室动 (VF) 是全球死亡的主要原因之一.
- 心率变化 (HRV) 作为检测和预测危险心律失常的关键生物标志物.
- 早期预测VF对于预防心脏突然死亡至关重要.
研究的目的:
- 评估HRV特征在预测心室的发作中的有效性.
- 为了确定最佳的HRV数据长度和预测时间,用于VF预测.
- 评估人工神经网络 (ANN) 用于使用HRV进行VF预测的性能.
主要方法:
- 从七个不同的HRV数据长度中提取特征.
- 使用了一种人工神经网络分类器,经过10倍交叉验证进行训练和验证.
- 测试了9个不同的预测时间的预测准确度.
主要成果:
- 最高预测准确率为88.18%和88.64%,分别是10s和20s HRV数据长度,预测时间为0s.
- 最低的预测准确性是在70年代HRV数据长度和80年代预测时间观察到的.
- 接近VF发作的HRV信号的特征显示出高预测能力.
结论:
- HRV分析,特别是使用事件前的短数据段,可以有效地预测心室动.
- 人工神经网络适合根据HRV特征预测VF.
- 近事件 HRV 功能为实时 VF 预测和预防策略提供了一个有希望的方法.
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