一个基于Sparrow搜索算法的新型心电图信号否定算法,用于最佳变量模态分解
Jiandong Mao1,2, Zhiyuan Li1,2, Shun Li1,2
1School of Electrical and Information Engineering, North Minzu University, North Wenchang Road, Yinchuan 750021, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
这项研究介绍了VMD-SSA-SVD,这是一个有效的方法来消除心电图 (ECG) 信号. 它显著减少噪音和基线漂移,同时保持关键的心电图信号特征.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 心血管疾病的诊断 心血管疾病的诊断
背景情况:
- 电心电图 (ECG) 信号处理对于预防和诊断心血管疾病至关重要.
- 电图信号容易受到来自设备,环境和传输的噪音干扰.
研究的目的:
- 为ECG信号提出一种高效的无声化方法.
- 为了解决噪声干扰和基线偏移在心电图信号.
主要方法:
- 开发了一种新的VMD-SSA-SVD算法,它结合了变量模态分解 (VMD),搜索算法 (SSA) 和单值值分解 (SVD).
- 对于信号分解,SSA优化了VMD参数[K,α].
- 消除了基线漂移组件,并使用SVD处理有效模式以减少噪音和重建信号.
主要成果:
- 与波段包分解,EMD,EEMD和CEEMDAN相比,VMD-SSA-SVD算法显示出优异的降噪效果.
- 该方法有效地抑制了噪音,并消除了基线漂移.
- 保存了心电图信号的关键形态特征.
结论:
- VMD-SSA-SVD算法在ECG信号消噪方面取得了重大进展.
- 这种技术提高了ECG分析用于心血管疾病诊断的可靠性.
- 该方法为清理杂的心电图数据提供了强大的解决方案.
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