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使用深度特征融合和机器学习分类与调整加权超参数的KPCA-WRF心率预测
G Jasmine Christabel1,2, A C Subhajini2
1Department of Computer Application, Noorul Islam Center for Higher Education, Kumaracoil, India.
Network (Bristol, England)
|August 3, 2023
概括
这项研究引入了一种新的实时心率预测模型,KPCA-WRF,将K-最近邻居,PCA和加权随机森林与深度CNN相结合,以提高高维数据的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 像人工智能和深度学习这样的先进技术对于心率预测至关重要.
- 现有的方法在高维数据和深度特征学习方面扎.
- 改善实时心率监测对于医疗保健至关重要.
研究的目的:
- 提出一种新的实时心率预测模型.
- 解决现有方法在处理高维数据和深度特征学习方面的局限性.
- 为了提高心率检测的准确性和效率.
主要方法:
- 一种混合方法 (KPCA-WRF) 结合K-近邻 (KNN),主要组件分析 (PCA) 和加权随机森林 (WRF) 进行特征融合.
- 集成深度卷积神经网络 (CNN) 进行深度特征学习.
- 使用群优化 (ACO) 和粒子群优化 (PSO) 优化特征选择的优化.
- 在优化功能上使用PCA减少尺寸.
- 融合特征的分类和超参数调整使用随机森林与K折验证.
主要成果:
- 拟议的KPCA-WRF模型与深度CNN相结合,可以有效地处理高维数据集.
- 使用 ACO 和 PSO 的特征选择和优化显著增强了特征表示.
- PCA有效地减少了维度,同时保留了重要的心率特征.
- 该模型在实时心率预测方面表现得更好.
结论:
- 与深度CNN集成的KPCA-WRF方法为实时心率预测提供了强大的解决方案.
- 功能融合,深度学习和优化算法的结合提高了预测准确度.
- 这种方法为非侵入性心率监测系统提供了有前途的进步.
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