Title :
ECG denoising and compression by sparse 2D separable transform with overcomplete mixed dictionaries
Author :
Ghaffari, A. ; Palangi, H. ; Babaie-Zadeh, M. ; Jutten, C.
Author_Institution :
Electr. Eng. Dept., Sharif Univ. of Technol., Tehran, Iran
Abstract :
In this paper, an algorithm for ECG denoising and compression based on a sparse separable 2-dimensional transform for both complete and overcomplete dictionaries is studied. For overcomplete dictionary we have used the combination of two complete dictionaries. The experimental results obtained by the algorithm for both complete and overcomplete transforms are compared to soft thresholding (for denoising) and wavelet db9/7 (for compression). It is experimentally shown that the algorithm outperforms soft thresholding for about 4 dBor more and also outperforms Extended Kalman Smoother filtering for about 2 dB in higher input SNRs. The idea of the algorithm is also studied for ECG compression, however it does not result in better compression ratios than wavelet compression.
Keywords :
Kalman filters; data compression; electrocardiography; image denoising; medical signal processing; ECG compression; ECG denoising; Extended Kalman Smoother filtering; overcomplete mixed dictionaries; soft thresholding; sparse 2D separable transform; Bayesian methods; Cardiovascular diseases; Dictionaries; Discrete cosine transforms; Electrocardiography; Filtering algorithms; Kalman filters; Noise reduction; Wavelet transforms; Wiener filter; ECG Compression; ECG Denosing; Sparse Coding; Sparse Decomposition; Sparse Representation;
Conference_Titel :
Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4947-7
Electronic_ISBN :
978-1-4244-4948-4
DOI :
10.1109/MLSP.2009.5306223
