对高血压的因果推断 预测可穿戴的E心电图和P热电图信号:可行性研究
Ke Gon G1, Yifan Chen1, Xinyue Song1
1School of Life Science and Technology, University of Electronic Science and Technology of China, Research Building C348A, 3rd Fl, Chengdu, 611731, China, 86 18030493605.
JMIR cardio
|January 26, 2025
概括
从心电图 (ECG) 和光电图 (PPG) 信号的因果推断可以准确预测高血压风险. 这种使用因果特征的方法优于基于关联的方法,可靠地检测高血压.
科学领域:
- 心血管疾病的研究研究.
- 生物医学信号处理
- 机器学习在医疗保健中的应用
背景情况:
- 高血压是全球主要的健康问题,增加了心血管疾病风险和死亡率.
- 早期发现和管理高血压对于公共健康至关重要.
- 来自可穿戴传感器的心电图 (ECG) 和光电图 (PPG) 信号为及时检测高血压提供了潜力.
研究的目的:
- 通过因果推理方法,研究预测高血压风险的可行性.
- 探索与高血压有因果关系的特征,超越单纯的相关性.
- 为了验证因果关系的可靠性和有效性与高血压检测中的相关性相比.
主要方法:
- 利用一个大型的公共数据集 (Aurora项目),同时从可穿戴设备获得ECG和PPG信号.
- 提取了205个特征,根据6个统计指标选择了有价值的特征,并构建了因果图.
- 融合因果图用于识别基于机器学习的高血压检测的因果相关特征.
主要成果:
- 从405名受试者中确定了与高血压相关的24个因果特征.
- 在高血压检测中,因果特征实现了89%的准确性,92%的精度和82%的回忆.
- 性能优于相关性特征,其准确度为85%,精度为88%,回忆率为77%.
结论:
- 因果推断为使用非侵入性信号检测高血压提供了一种可靠和有效的方法.
- 这种方法可以澄清高血压检测的潜在机制.
- 因果关系在高血压检测和类似应用中被证明比相关性更强大.
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