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使用独立的矢量分析将多个心电图文物分离出来

Zahoor Uddin¹, Muhammad Altaf¹, Ayaz Ahmad¹

¹Electrical & Computer Engineering, COMSATS Univeristy Islamabad-Wah Campus, Wah Cantt, Punjab, Pakistan.

PeerJ. Computer science

|June 22, 2023

概括

独立向量分析 (IVA) 有效地从心电图 (ECG) 信号中删除了物体,通过最小地改变原始的ECG数据,优于传统方法,如独立组件分析 (ICA) 和正规相关性分析 (CCA).

科学领域:

生物医学工程生物医学工程
信号处理信号处理
医疗信息学医疗信息学

背景情况:

电心电图 (ECG) 信号经常被生理和非生理学文物损坏.
基线流浪,电极运动和肌肉工件对心电图信号质量构成重大挑战.
独立组件分析 (ICA) 是一个常见的方法来移除心电图文物,但存在限制.

研究的目的:

引入和评估独立向量分析 (IVA) 以提高心电图信号中的器件去除.
将IVA的性能与ICA和正规相关性分析 (CCA) 等既定方法进行比较.
通过使用记录信号及其延迟版本来证明IVA的实际实用性.

主要方法:

独立矢量分析 (IVA) 用于从心电图数据中删除文物.
IVA利用了二次统计 (规范相关性分析 - CCA) 和高阶统计 (独立组件分析 - ICA).
该方法利用记录的信号及其时间延迟的对应物来改进不混合.

主要成果:

与CCA和ICA相比,拟议的IVA技术在物件移除方面表现出优异的性能.
在保持原始心电图信号的完整性同时,IVA成功地消除了人工制造物.
对真实和模拟心电图数据的评估证实了IVA方法的有效性.

结论:

独立向量分析 (IVA) 提供了一种更有效的解决方案,用于ECG人工物去除.
IVA提供了一种实用且可靠的方法来提高心电图信号质量.
拟议的技术尽量减少在人工物消除过程中对基础ECG形态的改变.

关键词:

移除文物移除文物盲源分离器的盲源分离方式电心电图 (ECG) 是一种心电图.独立组件分析独立组件分析独立的矢量分析.

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