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一个基于SNMF-DCNN的多通道UNet框架,用于强大的心肺声音分离

Weibo Wang¹, Dimei Qin¹, Shubo Wang¹

¹College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China.

Computers in biology and medicine

|July 27, 2023

概括

这项研究引入了一种用于分离心脏和肺部声音的新方法,提高了心肺疾病的诊断准确度. 这种新方法提高了医疗专业人员的听觉效率和可靠性.

科学领域:

生物医学工程生物医学工程
医学声学医学声学
信号处理信号处理

背景情况:

心肺疾病对全球健康构成重大风险,需要改进诊断工具.
使用电子耳镜进行听觉会捕获心脏声音 (HS) 和肺部声音 (LS),但它们的干扰会阻碍准确的诊断.
现有的方法难以处理HS和LS的时间和频率重叠.

研究的目的:

开发一种有效的策略,将心脏和肺部声音从组合录音中分离出来.
通过改进声音分析,提高心肺疾病查的准确性和效率.
为了应对诊断听觉中心脏和肺部声音之间的相互干扰的挑战.

主要方法:

提出了一种新的盲源分离 (BSS) 策略,从心肺声音 (HLS) 分类开始.
使用稀疏非负矩阵因子化 (SNMF) 来从HLS中提取肺声 (LS) 特性.
一个扩展卷积神经网络 (DCNN) 根据LS大小特征将HLS分为五种类型.
一个多通道UNet (MCUNet) 模型对每个分类的HLS类别进行了声音分离.

主要成果:

该研究成功地使用SNMF-DCNN方法对HLS进行了分类.
在MCUNet模型中,每个HLS类别的HS和LS被有效地分开.
与现有的最先进的方法相比,拟议的框架实现了更高的分离质量和稳定性.

结论:

这项工作介绍了第一个HLS分类方法 (SNMF-DCNN) 并将UNet应用于心肺声音分离.
开发的BSS策略通过准确分离HS和LS,显著提高了听觉诊断的诊断潜力.
这些发现提供了一种更强大,更有效的方法来分析心肺声音用于疾病查.

关键词:

心血管疾病的心血管疾病.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.稀少的非负矩阵分解因子.联合国网络联合国网络联合国网络

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