Key result
A novel wavelet-based unsupervised learning method enabled better ECG-EMG separation quality than reference methods across multiple spectral and energy metrics.
A novel wavelet-based unsupervised learning method improves the removal of ECG artifacts from single-channel surface EMG recordings without requiring a priori knowledge or parameter adjustment.
We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods.
No takes yet. Share an insight, caveat, or question.
Niegowski et al. (2016) studied Electrocardiogram (ECG) perturbation in surface electromyogram (EMG) recordings. Wavelet-based unsupervised learning method with non-negative matrix factorization vs. Two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method was evaluated on ECG-EMG separation quality (high-to-low energy ratio, normalized median frequency, spectral power difference, normalized average rectified value). A novel wavelet-based unsupervised learning method enabled better ECG-EMG separation quality than reference methods across multiple spectral and energy metrics.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: