结合手腕脉冲和心电图数据,可以更好地识别冠心病及其并发症
Lei-Xin Hong1, Wen-Jie Wu1, Xia Chen2
1School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Frontiers in physiology
|August 13, 2025
概括
同步获取手腕压力脉冲波 (PPW) 和心电图 (ECG) 信号,结合特征选择,显著改善冠心病 (CHD) 识别. 这种方法可以提高心血管疾病的诊断准确度.
科学领域:
- 生物医学工程 生物医学工程
- 心血管研究研究心血管研究
- 医疗保健中的机器学习
背景情况:
- 冠状动脉心脏病 (CHD) 构成了全球健康的重大负担.
- 准确和早期识别CHD及其并发症对于有效管理至关重要.
- 目前的诊断方法可能是侵入性的或需要专门的设置.
研究的目的:
- 调查结合手腕压力脉冲波 (PPW) 和四肢电心电图 (ECG) 信号用于心脏病识别的疗效.
- 开发和评估一种机器学习模型,用于使用这些同步信号预测心脏病和相关疾病.
- 评估特征选择对模型性能的影响.
主要方法:
- 同步获取手腕PPW和四肢的ECG信号从748名参与者使用定制设备.
- 从PPW和ECG信号中提取不同的特征.
- 开发和比较随机森林 (RF),特征选择RF (FS-RF),支持矢量机 (SVM) 和袋装决策树 (BDT) 模型.
- 基于准确性,精度,回忆和F1分数的模型评估.
主要成果:
- 集成PPW和ECG的射频模型具有使用单个信号类型的表现优于单个信号类型的模型.
- 该FS-RF模型表现出卓越的性能,达到76.32%的精度,75.82%的平均精度,76.11%的平均回忆率和75.88%的平均F1得分.
- 最佳的特征选择包括了PPW和ECG信号的特征.
结论:
- 同步获取PPW和ECG信号,加上有效的特征选择,显著提高了机器学习模型用于CHD识别的性能.
- 开发的FS-RF模型显示了对心血管疾病的早期检测和管理的前景.
- 研究结果支持将可穿戴设备用于临床应用在心血管健康监测中的整合.
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