利用启发式优化利用PPG信号检测心血管疾病的分类器性能
Sivamani Palanisamy1, Harikumar Rajaguru2
1Department of Electronics and Communication Engineering, Jansons Institute of Technology, Coimbatore 641659, India.
Diagnostics (Basel, Switzerland)
|October 25, 2024
概括
启发式优化和机器学习有效地使用光聚光学 (PPG) 信号检测心血管疾病 (CVD). 这种方法在非侵入性地识别心血管疾病方面达到95.12%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
- 心血管健康 心血管健康
背景情况:
- 摄影致血学 (PPG) 信号测量血液体积变化,并越来越多地用于非侵入性心血管疾病 (CVD) 检测.
- 分析PPG信号有助于识别不规则的心脏模式和其他心血管疾病指标.
研究的目的:
- 评估启发式优化算法的有效性与机器学习分类器相结合,用于使用PPG信号进行非侵入性心血管疾病检测.
- 确定缩小维度和分类技术的最佳组合,以准确诊断心血管疾病.
主要方法:
- 利用来自CapnoBase数据库的41个受试者的PPG数据 (21个正常,20个心血管疾病).
- 应用了启发式优化算法 (ABC-PSO,CSA,DFA) 来减少PPG数据的维度.
- 通过各种机器学习模型 (LR,KNN,LDA,SVM变体) 分类缩小尺寸的PPG数据,并通过准确性,MCC和F1得分等指标评估性能.
主要成果:
- 应用于ABC-PSO缩小维度数据的SVM-RBF分类器实现了最高准确率 (95.12%) 和最低错误率 (4.88%).
- 这种最佳模型还产生了高性能指标:MCC和卡帕 (0.90),GDR和F1得分 (95%),和贾卡德指数 (90.48%).
结论:
- 基于启发式的优化和机器学习的PPG信号的分类证明了高效的非侵入性心血管疾病检测.
- 这项研究证实了先进的计算方法在改善心血管诊断方面的潜力.
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