使用基于音频的分析与机器学习的肺部疾病识别方法
Ahmad H Sabry1, Omar I Dallal Bashi2, N H Nik Ali3
1Department of Medical Instrumentation Engineering Techniques, Shatt Al-Arab University College, Basra, Iraq.
Heliyon
|February 29, 2024
概括
肺部声音的计算机分析通过减少主观性来改善呼吸道疾病的诊断. 机器学习对分类肺部疾病充满希望,但需要进行大规模研究才能在临床采用.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
- 信号处理 信号处理
背景情况:
- 基于计算机的自动化方法和先进的记录技术增强了肺部声音诊断,最大限度地减少了主观性.
- 基于计算机的肺声分析能够彻底评估肺声特征,包括行为分析,测量,噪声抑制和图形表示.
研究的目的:
- 提供基于计算机的肺声分析用于呼吸系统疾病诊断的概述.
- 讨论在肺声分析中使用的方法,数据集,特征提取,预处理,文物删除,声音分离和机器学习算法.
- 确定文献上的差距,并突出机器学习在分类呼吸系统疾病方面的潜力.
主要方法:
- 文献综述和现有关于基于声音的肺部疾病分类研究的调查.
- 讨论机器学习算法,包括深度学习和波形变换,应用于肺部音频信号.
- 数据集的分析,特征提取,预处理和文物移除技术.
主要成果:
- 应用于肺声分析的机器学习算法在呼吸系统疾病分类方面显示出有希望的结果.
- 该研究确定了基于声音的肺部疾病诊断的关键元素和方法.
- 突出了临床采用大规模研究的文献差距.
结论:
- 基于声音的机器学习为呼吸系统疾病的分类提供了一个有前途的途径.
- 进一步的大规模调查对于验证发现和促进广泛临床采用至关重要.
- 这些发现对医生和研究人员在基于声音信号的机器学习领域有价值.
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