根据图形特征对支气管和肺部擦声信号进行分类:复杂网络未包裹的潜力
Ammini Renjini1, Mohanachandran Nair Sindhu Swapna2, Sankaranarayana Iyer Sankararaman3
1Department of Optoelectronics, University of Kerala, Trivandrum, Kerala, 695581, India.
Physical and engineering sciences in medicine
|July 2, 2024
概括
这项研究使用图形理论来分析肺部声音,区分支气管 (BS) 和肺部摩擦 (PS) 声音,以早期检测呼吸系统疾病. 机器学习模型在分类这些声音方面达到90%以上的准确性.
科学领域:
- 医学声学 医学声学
- 图形理论应用 图形理论应用
- 生物医学信号处理
背景情况:
- 肺部听觉对于诊断呼吸系统疾病至关重要.
- 区分正常和异常的肺声音,如支气管 (BS) 和肺 (PS) 声音,有助于早期发现疾病.
- 传统的听术方法可能是主观的,缺乏定量分析.
研究的目的:
- 通过使用图形理论,引入一种新的肺听技术.
- 探索图表参数在区分BS和PS肺声中的潜力.
- 通过先进的信号分析来支持早期检测呼吸道病理.
主要方法:
- 使用功率光谱密度 (PSD) 和波纹声谱图分析了85个BS和PS的肺声.
- 提取复杂的网络图形特征,包括密度,过渡性和中心性指标.
- 监督机器学习算法的应用:区分分析,支持向量机器,k-最近邻居和模式识别神经网络 (PRNN).
主要成果:
- 对于BS和PS的声音,确定了不同的频率分布和组件大小.
- 高图形密度和过渡度与来自均气道的BS声音相关联.
- 由于膜摩擦,在PS信号中观察到图表指标的高值,如间距和自身向量的中心性.
- 机器学习模型实现了>90%的分类准确性,PRNN达到98.82%.
结论:
- 图形理论参数有效地区分支气管和肺部声.
- 这种新的方法为肺声分析提供了一种定量方法.
- 这些发现支持使用基于图形的机器学习来更早,更准确地诊断呼吸系统疾病的潜力.
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