复杂的音频信号数据压缩和重建:用于慢性呼吸道疾病的机器分类的基准数据预处理方法
Timothy Albiges1, Zoheir Sabeur1, Banafshe Arbab-Zavar1
1Department of Computing and Informatics, Bournemouth University, Bournemouth, UK.
Digital health
|December 11, 2024
概括
这项研究引入了用于医疗听觉的先进音频信号压缩,多分辨率波波变换 (MRWT) 显示了最佳结果. MRWT有效地平衡了压缩和重建准确性,以确保诊断完整性.
科学领域:
- 医疗信号处理 医疗信号处理
- 生物医学工程 生物医学工程
- 数字信号处理是数字信号处理.
背景情况:
- 医学听觉产生复杂的音频信号,对诊断至关重要.
- 有效地压缩这些信号对于机器学习分类和数据管理至关重要.
- 现有的方法可能在压缩过程中难以保持诊断完整性.
研究的目的:
- 开发和评估用于压缩和重建医疗听觉音频信号的新方法.
- 为了保持诊断准确度,同时减少用于机器分类的信号维度.
- 为此目的,评估各种信号处理技术的有效性.
主要方法:
- 使用ICBHI呼吸挑战2017数据库进行分析.
- 评估了压缩框架,包括离散里埃变换,时间频率变换,字典学习和单数值分解.
- 使用平均平方误差 (MSE) 评估重建质量.
主要成果:
- 多分辨率波形变换 (MRWT) 框架实现了0.037.7的最低平均MSE.
- 拟议的时间频率框架包含MRWT,在区分慢性阻塞性肺病与健康的呼吸道声音方面显示出80%的准确性.
- 在评估的压缩方法中,MRWT被证明是优越的.
结论:
- 该研究推进了用于医疗听觉的信号处理技术.
- 在压缩效率和重建准确性之间,MRWT方法提供了一个有前途的平衡.
- 这些发现提供了关于在压缩音频信号中保存诊断信息的见解.
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