通过使用中位数EEMD-Hurst和值否定方法对先天性心脏病进行心声分类
Xuankai Yang1, Jing Sun1, Hongbo Yang2
1School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
Medical & biological engineering & computing
|August 4, 2024
概括
这项研究引入了一种使用中间集体实证模式分解 (MEEMD) 和神经网络准确分类心脏声音的新方法,通过减少噪音干扰来改善先天性心脏病诊断.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能在医学中的应用
背景情况:
- 心脏声音分析对于诊断先天性心脏病至关重要.
- 噪音在心声采集中的噪音显著降低了诊断准确度.
- 现有的降噪方法有风险去除弱弱的病理性心脏声音组件.
研究的目的:
- 为心脏声信号开发一个强大的降噪和分类方法.
- 为了提高先天性心脏病的机器辅助诊断的准确性.
- 解决现有无声化技术的局限性,这些技术可能会过出病理信号.
主要方法:
- 中位集体经验模式分解 (MEEMD) 用于信号分解.
- 赫斯特分析以确定噪音占主导地位的内在模式函数 (IMF).
- 对已识别的IMF实施了改进的门撤销,随后进行信号重建.
- 卷积神经网络 (CNN) 用于使用Mel光谱系数对表达信号进行分类.
主要成果:
- 在信号分解过程中,MEEMD有效地抑制了模式混合和分割.
- 这种新的消毒方法成功地保留了病理成分.
- 对正常和异常心脏声音的分类实现了93.8%的准确性,93.1%的特异性和94.6%的灵敏度.
结论:
- 提出的基于MEEMD的无声化和CNN分类方法为心脏声音分析提供了显著的改进.
- 这种方法提高了先天性心脏病机器辅助诊断的可靠性.
- 该技术有效地减少噪音,同时保留心脏声信号中的关键病理信息.
相关概念视频
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S1 (First Heart Sound)-
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