一个双分支功能提取网络用于心脏声音信号分析
1School of Information Engineering, Nantong Institute of Technology, Nantong, China. chenhao@ntit.edu.cn.
Scientific reports
|July 30, 2025
概括
一个新的双分支网络,HSDFE-Net,有效地分析复杂的心声信号,用于早期心血管疾病诊断. 这种方法通过结合传统音频和非线性双频谱特征来提高识别精度.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 准确分析心脏声音信号对于早期心血管疾病诊断至关重要.
- 信号的复杂性和多样性对当前的识别方法构成重大挑战.
研究的目的:
- 引入一种新的双分支特征提取网络 (HSDFE-Net),以改进心脏声音识别.
- 解决传统单分支网络在捕获全面信号特征方面的局限性.
主要方法:
- 该HSDFE-Net模型从心脏声音信号中提取了传统的音频功能和双频谱功能.
- 双分支架构将这些特征单独处理:一个分支用于非线性双频谱特征,另一个分支用于传统音频特征.
- 一个挤压和激发模块可自适应地增强传统音频分支中的功能通道.
主要成果:
- 在三个公共数据集上,HSDFE-Net 实现了 99.00%,99.53% 和 83.33% 的高精度.
- 双分支方法有效地描述了心声信号的线性光谱和非线性特性.
- 互补特征的融合导致了对信号特征的更全面的理解.
结论:
- HSDFE-Net在心脏声音分析方面表现出有效性和稳定性.
- 拟议的网络为准确的心脏声音识别提供了一个有希望的解决方案.
- 这种方法可以帮助早期诊断心血管疾病.
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