混合DWT NLM方法与NOA优化为ECG信号消噪
Wen Chen1, Yuanfang Zhang2, Kaimin Yu3
1School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China. 202061000040@jmu.edu.cn.
Scientific reports
|July 7, 2025
概括
这项研究引入了一种新的坚果破碎机优化算法 (NOA) 增强的离散波段转换+非局部平均值 (DWT+NLM) 方法,用于改进心电图 (ECG) 信号消噪. 优化的框架在各种噪声条件下显著提高了信号噪声比 (SNR),提高了心血管疾病诊断的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 心血管诊断心血管诊断服务
背景情况:
- 传统的离散波形变换+非局部平均值 (DWT+NLM) 方法用于心电图 (ECG) 信号处理,存在翻译差异,模态别名,补丁效应和值扭曲.
- 这些文物损害了基于心电图数据的心血管疾病诊断的准确性.
研究的目的:
- 介绍一个坚果破碎机优化算法 (NOA) 增强的DWT+NLM框架,用于改进心电图信号消噪.
- 动态优化波纹分解和非局部平均值 (NLM) 参数,以提高噪声降低和人工物缓解.
主要方法:
- 拟议的框架使用了破子优化算法 (NOA) 来优化波形分解水平和基础函数.
- 对NLM参数的自适应调整和引入Sigmoid调整的值函数可以减轻补丁效应和恒定偏差.
- 在Physionet数据集上进行了实验,评估了对添加式白高斯噪声 (AWGN) 和现实世界的噪声 (基线漫步,肌肉工件,电极运动工件) 的性能.
主要成果:
- 通过NOA增强的DWT+NLM方法,实现了AWGN的最大信号噪声比 (SNR) 增益2.42dB.
- 在现实噪声场景中,该方法比第二个最佳方法平均提高了3.12dB的SNR.
- 证明了强大的适应性和在减少ECG信号中各种噪音类型的实际优势.
结论:
- 破子优化算法 (NOA) 显著提高了DWT+NLM在ECG信号消噪方面的性能.
- 增强的框架提供了强大的降噪能力,这对于准确的心血管疾病诊断至关重要.
- 这种方法显示了将其集成到可穿戴式心电图传感器中的潜力,以提高诊断准确度.
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