A Localized ECG Cancellation Method for Preserving EMG Information in Trunk Muscle Analysis
Keywords:
ECG Cancellation, Quasi-periodic Signals Detection, Discrete Wavelet Transforms, Independent Component Analysis.Abstract
Surface electromyography (sEMG) signals from the trunk region are often distorted by the electrical activity of the heart (ECG), particularly when analyzing low-amplitude EMG responses. Existing methods for resolving this problem through ECG cancellation often lead to the deterioration of noiseless portions of the signal. In this paper, we propose an original ECG cancellation method that aims to limit the deterioration of sEMG information. Instead of directly removing the ECG, our method consists of two main steps: ECG localization and selective ECG cancellation based on detected heart pulses. The localization phase effectively extracts the ECG contribution using a combination of discrete wavelet transforms (DWT) and independent component analysis (ICA). Subsequently, a novel algorithm based on the fast Fourier transform (FFT) leverages the quasi-periodic properties of the ECG to accurately detect the positions of heart pulses. We conducted extensive simulations to evaluate the proposed method in terms of relative errors, coherence, and accuracy under different levels of ECG interference. Additionally, we assessed the method using correlation coefficients computed from paraspinal muscle EMG signals obtained from 12 healthy participants. The simulation and real data results demonstrate that our proposed method accurately detects pulse positions, efficiently removes ECG from EMG signals even in heavily overlapped scenarios, and effectively limits EMG deterioration.
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