机器学习技术用于提高多个分类器在检测心血管疾病从PPG信号的性能
Sivamani Palanisamy1, Harikumar Rajaguru2
1Department of Electronics and Communication Engineering, Jansons Institute of Technology, Coimbatore 641659, India.
Bioengineering (Basel, Switzerland)
|June 28, 2023
概括
机器学习增强了从光电显微镜 (PPG) 信号的心血管疾病 (CVD) 检测. 希尔伯特变换与子搜索实现了98.31%的准确性,提供了一个有前途的非侵入性诊断工具.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 摄影致血镜 (PPG) 是一种非侵入性,具有成本效益的临床诊断工具.
- 心血管疾病 (CVD) 的检测通常依赖于复杂而昂贵的方法.
- 由于数据大小,PPG信号存在挑战,需要减少维度.
研究的目的:
- 提高机器学习分类器的性能,用于使用PPG信号检测CVD.
- 在PPG数据上研究各种维度减小技术的有效性.
- 确定缩小维度和分类的最佳组合,以准确诊断心血管疾病.
主要方法:
- 使用启发式 (ABC-PSO,古柯群,群) 和基于转换的 (希尔伯特转换,非线性回归) 技术,应用于PPG信号的尺寸缩小.
- 对12个不同的分类器 (例如PCA,逻辑回归,波搜索,PAC贝叶斯学习) 在缩小尺寸的PPG数据上进行评估.
- 基于准确性,性能指数,错误率和所有分类器-技术组合的良好检测率的性能评估.
主要成果:
- 希尔伯特变换与波搜索分类器相结合,证明了卓越的性能.
- 这种组合实现了98.31%的准确性和96.55%的良好检测率.
- 评估了其他几个缩小维度和分类器组合,不同程度的成功.
结论:
- 希尔伯特变换和和性搜索分类器的组合提供了一个非常准确的方法,用于从PPG信号中进行非侵入性CVD检测.
- 减小尺寸对于高效处理大型PPG数据集至关重要.
- 这种方法有可能在心血管健康监测中得到广泛的临床应用.
关键词:
在ABC的公共事业部门,ABC是公共事业部门.贝叶斯线性差异分析分类器在CVD中,CVD是非常重要的.高斯混合物模型模型的高斯混合物模型.希尔伯特变换是一个变换.预期最大化 期望最大化调和式搜索 调和式搜索非线性回归是一种非线性回归.分析组件的原则分析组件.更多相关视频
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