Application of Independent Component Analysis to ECG Cancellation in Surface Electromyography Measurement

Abstract

Surface electromyogram usually incurs severe influence of strong electrocardiography (ECG) signal. This paper presents a novel denoise method using independent component analysis (ICA) to remove the interference of ECG from sEMG recordings. The present study removed the ECG in independent components decomposed from the sEMG by a high-pass filter that kept the useful information as more as possible during ECG cancellation. The filtering ICA ECG cancellation results were compared with the simulated pure sEMG in time and frequency domain, which suggested that the ICA ECG cancellation with 30Hz high-pass filter would be the most appropriate method to extract the useful sEMG signals from multi-channel sEMG measurement. The kurtosis is used to measure the nongaussianity of signal; it may be used as a standand for automatic determination of the independents of ECG or EMG.

體表肌電特別是從軀干獲得的體表肌電往往受到被測對象自身心電信號的嚴重干擾。本文利用一種基于獨立分量分析(ICA)的去噪方法,去除體表肌電中的心電干擾。該方法將多通道體表肌電進行獨立分量分解,并用高通濾波器處理所分解出的心電獨立分量以盡可能地保留其中的肌電成分,進而將去除心電干擾后的所有獨立分量反向投影回原始信號空間得到去噪后的信號。仿真信號的處理結果表明,當高通濾波器的截止頻率為30Hz時,該方法在有效去除心電干擾的同時使體表肌電的保真度達到最大。同時討論了將信號的峰度(Kurtosis)值作為自動判別心電分量和肌電分量的標準的可能性。