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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
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一个高效的肺声分离算法基于GIHO-VMD.

Leiming Zhang1, Fuliang He1, Hao Tan1

  • 1College of Electronic and Information Engineering, Southwest University, 400715, Chongqing, China.

Computer methods and programs in biomedicine
|October 24, 2025
PubMed
概括

这项研究引入了一种新的算法,用于将肺部声音 (LS) 与心脏声音 (HS) 分开. 基于引导改善河马优化的变化模式分解 (GIHO-VMD) 方法通过改善肺声信号分解来提高诊断准确性.

科学领域:

  • 生物医学工程 生物医学工程
  • 信号处理 信号处理
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 肺部声音 (LS) 对于诊断肺部疾病至关重要.
  • 临床听觉受到心脏声音 (HS) 和噪音的阻碍,损害了准确性.
  • 频谱重叠和信号非静态性对LS分离构成挑战.

研究的目的:

  • 开发一种先进的算法,以准确分离肺部声音.
  • 克服传统方法在提取病理性LS的局限性.
  • 提高肺声分析用于疾病诊断的质量和可靠性.

主要方法:

  • 提出了一种基于指导改进河马优化的变化模式分解 (GIHO-VMD) 算法.
  • 使用模糊 (FE) 作为具有新奇复杂性约束的目标函数.
  • 整合了颠覆运营商 (DO) 和指导学习策略 (GLS) 以提高优化和融合.

主要成果:

  • 在GIHO-VMD方法成功地分离模拟的心脏和肺部的声音信号.
  • 实现了正常LS的平均信号噪声比 (SNR) 30.27dB,异常LS的平均信号噪声比为 28.60-30.11dB.
  • 对于分离的LS,获得了平均63.15%的正常化交叉相关性 (NCC).
关键词:
干扰运营商 干扰运营商模糊的热量 模糊的热量指导式学习策略指导式学习策略河马优化算法 河马优化算法肺部声音分离 肺部声音分离变化模式分解的变化模式分解

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结论:

  • 拟议的GIHO-VMD方法展示了强大的肺声信号分离能力.
  • 有效地解决了从心脏和肺声音混合物中提取LS的挑战.
  • 显示了改善诊断肺声分解的概括性,质量和准确性的显著潜力.