基于进化优化的下降自适应过器用于心电图信号中的噪声没收
Shubham Yadav1, Suman Kumar Saha2, Rajib Kar3
1Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India. shubham.ydv@cgu-odisha.ac.in.
Physical and engineering sciences in medicine
|September 1, 2025
概括
这项研究引入了群集智能算法,以优化适应式噪声取消器 (ANC) 清理心电图 (ECG) 信号. 通过减少噪音和人工物, 提议的方法显著提高了心电图信号质量.
科学领域:
- 生物医学工程
- 信号处理
- 计算智能
背景情况:
- 电心电图 (ECG) 信号对于心脏诊断至关重要,但在记录过程中经常受到噪音和人工物的影响.
- 信号退化会损害心脏状况评估的准确性,需要有效的噪音消除技术.
研究的目的:
- 提出和评估基于群集智能的最佳调整的适应式噪声取消器 (ANC) 来消除心电图信号.
- 通过海优化算法 (SOA),NLSHADE和HGSA优化ANC的性能进行比较.
主要方法:
- 使用SOA,NLSHADE和HGSA优化ECG信号的ANC的开发.
- 使用公开的心电图数据集 (ADB,QTDB) 进行验证,这些数据集在各种SNR中被添加式白色高斯噪声损坏.
- 使用百分比根平均平方偏差 (PRD),平均平方误差 (MSE) 和信号对噪声比 (SNR) 改进等指标进行性能评估.
主要成果:
- 拟议的SOA辅助ANC以3.40E-03的PRD和1.35E-11的MSE实现了卓越的性能.
- 平均SNR改进为10.986dB,超过了基准算法.
- 使用Wilcoxon签名等级测试进行的统计验证证实了SOA辅助ANC的有效性.
结论:
- 群集智能优化,特别是SOA,为提高心电图信号质量提供了强大的方法.
- 拟议的ANC有效地消除了人工物,提高了心脏病诊断的准确性.
- 这项技术在临床应用中的生物医学信号处理方面取得了重大进展.
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