Automatic ECG artifact removal in the real-time SEMG recording system

2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2013-07-15 DOI:10.1109/CIVEMSA.2013.6617398

Yong Hu, Jerry Kwok, J. Tse

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Abstract

The contaminated electrocardiography (ECG) is a big problem in the surface electromyography (SEMG) signal detection and analysis. The objective of the current study is to propose and validate an algorithm for the automated feature cognition and identification for eliminating ECG artifact from the raw SEMG signals. The utilization of Independent Component Analysis (ICA) method is to decompose the raw SEMG signals into individual independent source components. After that, some of the independent source components with the characteristics of ECG artifact were detected by the automated identification algorithm and thereafter eliminated. The sensitivity and specificity of the algorithm for distinguishing ECG source components from independent source components are 100% and 99% respectively. The automated identification algorithm exhibits the prominent performance of recognition for ECG artifact and can be considered reliable and effective.

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实时表面肌电信号记录系统中心电伪影的自动去除

心电图污染是肌表电信号检测与分析中的一大难题。本研究的目的是提出并验证一种从原始表面肌电信号中消除心电伪影的自动特征认知和识别算法。利用独立分量分析(ICA)方法将原始表面肌电信号分解成独立的源分量。然后，通过自动识别算法检测出一些具有心电伪影特征的独立源分量，然后剔除。该算法区分心电源分量和独立源分量的灵敏度和特异性分别为100%和99%。该自动识别算法对心电伪信号的识别性能突出，可以认为是可靠有效的。

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2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

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