从呼吸声中排斥噪声的混合方法,使用短暂的工件减少算法和光谱减去算法
Nishi Shahnaj Haider1, Ajoy K Behera2
1Department of Electronics and Instrumentation Engineering, 154018 Ramaiah Institute of Technology , Bangalore, Karnataka, India.
Biomedizinische Technik. Biomedical engineering
|March 20, 2024
概括
这项研究引入了一种混合降噪方法,用于使用呼吸声来诊断呼吸系统疾病. 这种方法有效地清除噪音信号,提高COPD和喘等疾病的诊断准确度.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 呼吸系统医学 呼吸系统医学
背景情况:
- 自动呼吸系统疾病检测依赖于呼吸声分析.
- 杂的呼吸声信号损害了自动化系统的诊断准确性.
- 提出了一种新的混合方法,以减轻呼吸声数据中的噪音.
研究的目的:
- 开发和评估一种混合方法,以有效降低呼吸道声信号中的噪声.
- 提高呼吸声数据的质量,以提高诊断解释.
- 为了应对计算机化听觉系统中信号噪声的挑战.
主要方法:
- 记录了80名慢性阻塞性肺病 (COPD) 患者,75名喘患者和80名健康个体的呼吸声音.
- 应用了一种混合降噪技术,结合了Butterworth带通波器,短暂的工件减少和光谱减去.
- 使用信号对噪声比率 (SNR) 和峰值信号对噪声比率 (PSNR) 等指标评估噪声排斥性能.
主要成果:
- 混合算法实现了70dB的高信号噪声比 (SNR).
- 该算法显示了72dB的峰值信号噪声比 (PSNR).
- 在不同类别的呼吸声中证实了有效的噪音抑制.
结论:
- 拟议的混合方法在抑制来自呼吸声信号的噪音方面非常有效.
- 这种技术可以产生适合用于诊断应用的清洁呼吸声数据.
- 这些发现支持在自动呼吸道诊断系统中使用这种方法.
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