RT Journal Article
A1 Junjiang Zhu
A1 Xiaolu Li

PB iet
T1 Electrocardiograph signal denoising based on sparse decomposition
JN Healthcare Technology Letters
VO 4
IS 4
SP 134
OP 137
AB Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed. Then the model is solved by matching pursuit algorithm. In order to get better results, four kinds of dictionaries are investigated with the ECG signals from MIT-BIH arrhythmia database, compared with wavelet transform (WT)-based method. Signal–noise ratio (SNR) and mean square error (MSE) between estimated signal and original signal are used as indicators to evaluate the performance. The results show that by using the present method, the SNR is higher while the MSE between estimated signal and original signal is smaller.
K1 myoelectric interference
K1 ECG signal denoising
K1 MIT-BIH arrhythmia database
K1 sparse decomposition
K1 sparse-based method
K1 linear denoising method
K1 matching pursuit algorithm
DO https://doi.org/10.1049/htl.2016.0097
UL https://digital-library.theiet.org/;jsessionid=7u9it65n4c1ua.x-iet-live-01content/journals/10.1049/htl.2016.0097
LA English
SN
YR 2017
OL EN