一致的特征提取与集群智能基于混合的Adaboost加权ELM分类为打的声音分类
Sunil Kumar Prabhakar1, Harikumar Rajaguru2, Dong-Ok Won1
1Department of Artificial Intelligence Convergence, Chuncheon 24252, Republic of Korea.
Diagnostics (Basel, Switzerland)
|September 14, 2024
概括
准确的打分析对于诊断阻塞性睡眠呼吸暂停至关重要. 这项研究开发了使用离散波纹转换 (DWT) 功能和混合机器学习分类器的先进算法,在打声音分类中实现了高精度.
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:
- 打是阻塞性睡眠呼吸暂停 (OSA) 和睡眠相关呼吸障碍的常见症状,显著影响患者的生活质量.
- 准确的打检测和分类对于OSA诊断至关重要,需要高精度的自动化分析算法.
研究的目的:
- 开发和评估用于精确的打声音分析和分类的先进算法.
- 为了比较各种特征提取,选择和分类技术对打声音的有效性.
主要方法:
- 从六个领域提取特征:时间,频率,离散波段变换 (DWT),稀疏,自值和 cepstral.
- 使用金优化 (GEO),Salp Swarm算法 (SSA) 和精制的SSA进行了特征选择.
- 分类使用了八种传统的机器学习分类器和两个拟议的混合模型:火算法加权极端学习机器与Adaboost (FA-WELM-Adaboost) 和卡布奇人搜索算法加权极端学习机器与Adaboost (CSA-WELM-Adaboost).
主要成果:
- 使用DWT功能,用于功能选择的精细SSA和FA-WELM-Adaboost分类器实现了最佳性能,产生74.23%的未加权平均回忆率 (UAR).
- 用DWT功能,GEO功能选择和CSA-WELM-Adaboost分类器获得了第二好的结果,报告了73.86%的UAR.
结论:
- 混合机器学习模型,特别是FA-WELM-Adaboost和CSA-WELM-Adaboost,结合DWT功能和优化的选择技术,显示出对准确的打声音分类有很大的希望.
- 开发的方法通过精确的打分析,为阻塞性睡眠呼吸暂停的自动查和诊断提供了潜在的进步.
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