通过Mamba模型的弹性心电图信号进行非侵入性增强高血压检测
1Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia.
结合人工智能模型的胆心电图 (BCG) 提供了一种非侵入性的高血压检测方法. 马巴分类器实现了95.14%的准确性,显示了在家进行心血管监测的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心血管健康 心血管健康
背景情况:
- 高血压检测传统上依赖于侵入性方法.
- 需要使用非侵入性技术进行持续的,基于家庭的监测.
- 弹性心电图 (BCG) 可以捕捉与心脏相关的微振动.
研究的目的:
- 研究机器学习 (ML) 和深度学习 (DL) 模型在使用BCG信号检测高血压时的有效性.
- 为长期高血压监测开发一种非侵入性,高效的方法.
- 将新型DL架构 (Mamba,Transformer) 与传统ML模型的性能进行比较.
主要方法:
- 使用了128个BCG记录的数据集.
- 应用了各种分类模型:Mamba分类器,变压器,堆叠,投票和XGBoost.
- 基于准确性和曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 马巴分类器获得了最高的准确性 (95.14%) 和AUC (0.9922).
- 变压器和堆叠模型也表现出强的表现.
- 与AI集成的BCG信号显示出高血压检测的巨大潜力.
结论:
- 马巴分类器在使用BCG信号检测高血压方面非常有效.
- 通过人工智能增强的BCG技术,为持续的家庭健康监测提供了一个有希望的非侵入性替代方案.
- 建议在临床应用中使用更大的数据集进行进一步验证.
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