使用胆心仪信号和机器学习检测高血压的实用模型.
Sunil Kumar Prabhakar1, Dong-Ok Won1
1Department of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了两种机器学习模型,用于使用心电图 (BCG) 信号检测高血压. 最好的模型达到了96.89%的准确性,为高血压识别提供了一种新的方法.
科学领域:
- 生物医学信号处理
- 机器学习应用 机器学习应用
- 心血管健康 心血管健康
背景情况:
- 高血压是一种普遍的健康问题,与不健康的生活方式有关.
- 弹性心电图 (BCG) 信号为非侵入性高血压检测提供了潜力.
- 准确地解释BCG信号对于有效的分类至关重要.
研究的目的:
- 提出和评估两种机器学习模型,用于使用BCG信号检测高血压.
- 探索各种特征提取,选择和分类技术.
- 为了确定最佳的模型配置,以实现高分类准确度.
主要方法:
- 模型1:特征提取 (K-means,MODWT,EWT),选择 (BTSA,IG),并进行分类 (混合动力AdaBoost-MULDA,混合动力AdaBoost-RF).
- 模型2:特征提取 (PCA,KPCA,RFM),选择 (IG,AOA) 和分类 (混合ARIMA-AdaBoost,TW-HASVM) 的方法.
- 使用公开可用的BCG数据集进行模型验证.
主要成果:
- 这两种模型都在对BCG信号进行高血压检测的分类方面表现出有效性.
- 第二个模型,采用内核主要组件分析 (KPCA) 进行特征提取和Aquila优化算法 (AOA) 进行特征选择,显示出优异的性能.
- 混合自回归集成移动平均线 (ARIMA) -AdaBoost分类器实现了最高准确率96.89%.
结论:
- 拟议的机器学习方法,特别是KPCA-AOA-ARIMA-AdaBoost组合,显示出使用BCG信号精确检测高血压的重大前景.
- 这种方法为早期识别和管理高血压提供了一种有价值的非侵入性工具.
- 进一步的研究可以探索这些技术的改进,以更广泛的临床应用.
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