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量子LBP驱动的心声分析与在现实世界杂环境中的质量评估
Subhashree Sahoo1, Puneet Kumar Jain1
1Department of Computer Science & Engineering, National Institute of Technology Rourkela, Odisha, 769008, India.
Computer methods and programs in biomedicine
|September 29, 2025
概括
这项研究提出了一种新,高效和抗噪声的方法,用于分类心电图 (PCG) 心脏声音,改善偏远地区的心血管疾病诊断.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 心血管疾病 (CVD) 诊断依赖于昂贵的方法,如心电图和心声回声,限制了农村的访问.
- 声心图 (PCG) 提供了一个负担得起的替代方案,但容易受到噪音干扰.
- 现有的否定方法可以降低关键的心脏声音信息.
研究的目的:
- 开发一种计算效率高,耐噪声的PCG分类方法.
- 提高心脏声音分析的准确性和可访问性.
- 为资源有限的设置创建一个强大的诊断工具.
主要方法:
- 引入了一种质量评估指标 (PCGQA),用于选择最佳的PCG信号段.
- 采用增强量子局部二进制模式 (EQLBP) 来从光谱图中提取抗噪特征.
- 使用离散波纹转换 (DWT) 进行多分辨率时间频率分析,结合 EQLBP 功能.
- 在集成功能集上训练了传统的机器学习模型.
主要成果:
- 在公共数据集上实现了高性能:97.22%的准确性 (CinC-2016) 和98.70%的准确性 (HSM-2018).
- 与现有的PCG分析方法相比,证明了更高的精度,回忆和F1分数.
- 通过十倍交叉验证验证方法的有效性.
结论:
- 拟议的PCG分类方法在计算上是高效的,并且对噪声具有稳定性.
- 它的性能使其适用于临床以外的应用,特别是在农村和偏远地区.
- 为可访问的心血管诊断提供了一个有前途的解决方案.
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S2 (Second Heart Sound)-
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