HISET:混合可解释的策略与集体技术用于呼吸声分类的呼吸声分类
Sunil Kumar Prabhakar1, Dong-Ok Won1
1Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Gangwon-do, South Korea.
Heliyon
|August 9, 2023
概括
本研究介绍了五种用于自动化呼吸声分类的新型混合战略. 最好的方法,曼哈顿基于距离的变化模式分解与极端学习机器,实现95.39%的准确性为2类分类.
科学领域:
- 生物医学信号处理
- 机器学习 机器学习
- 呼吸系统医学 呼吸系统医学
背景情况:
- 呼吸系统声音的自动分类对于诊断呼吸系统疾病至关重要.
- 独特的声音与各种呼吸道疾病有关,这对信号处理构成了复杂的挑战.
研究的目的:
- 提出并评估五种混合可解释策略与合并技术 (HISET) 以进行强大的呼吸声分类.
- 为了比较这些新策略在ICBHI数据集上的表现.
主要方法:
- 使用格兰杰分析和支持集体实证模式分解 (SEEMD) 的集体GSSR技术与SVM-RFE.
- 领域修改稀疏表示分类 (RR-SRC).
- 用极端学习机器 (ELM) 进行距离度量依赖变化模式分解 (DM-VMD).
- 哈里斯·霍克斯优化 (HHO) 与基于缩放因子的柔性差异演化 (SFPDE) 用于ML分类.
- 用灰狼优化 (GWO-SVC) 和草优化算法 (GOA) 进行尺寸缩小. 基于Sparse自动编码器.
主要成果:
- 基于曼哈顿距离的VMD-ELM实现了95.39%的准确性,用于2类分类.
- 基于欧几里德距离的VMD-ELM在3类分类中实现了90.61%的准确性.
- 基于曼哈顿距离的VMD-ELM实现了89.27%的准确性,用于4类分类.
结论:
- 提出的HISET方法显示了自动化呼吸声分类的巨大潜力.
- 取决于距离计的VMD-ELM方法显示出卓越的性能,特别是曼哈顿距离变体用于2类和4类问题.
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