电心图和磁心图的预处理和排泄技术:一篇综述
Yifan Jia1,2, Hongyu Pei1,2, Jiaqi Liang1,2
1Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Bioengineering (Basel, Switzerland)
|November 27, 2024
概括
本综述探讨了用于心电图 (ECG) 和心磁图 (MCG) 的先进信号预处理,以改善心血管疾病 (CVD) 检测. 混合机器学习方法显示出增强denoising和诊断准确性的承诺.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 和磁心电图 (MCG) 对心血管疾病 (CVD) 的检测至关重要.
- 无论是ECG和MCG信号都容易受到各种噪音干扰,影响诊断的准确性.
- 缺乏对ECG和MCG无色化技术的系统比较.
研究的目的:
- 在过去十年中,系统地审查和分析ECG和MCG信号预处理方面的进展.
- 根据噪声类型 (基线漫游,EMG,PLI,复合噪声) 分类和比较ECG无声化方法.
- 检查MCG信号的挑战,并探索ECG和MCG的互补性质.
主要方法:
- 对ECG和MCG信号的预处理技术进行系统的文献审查.
- 基于噪声特征和信号类型的无声化方法的分类.
- 传统过,机器学习和混合方法的比较分析.
主要成果:
- 确定了ECG的各种无声化技术,以解决特定类型的噪音.
- 突出了MCG信号消噪的复杂性和挑战.
- 强调了ECG和MCG的互补作用以及MCG在改善心血管疾病诊断方面的潜力.
- 在临床环境中评估了当前染方法的局限性.
结论:
- 传统的过方法仍然很重要,但结合机器学习的混合策略提供了巨大的潜力.
- 未来的方向包括可解释和多任务神经网络,以提高报名和诊断的准确性.
- 这一审查为选择和增强消毒技术提供了一个框架,以改善心血管疾病诊断.
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