ECG Denoising and Compression by Sparse 2D Separable Transform with Over Complete Mixed Dictionaries
- Aboozar Ghafari ,
- Hamid Palangi ,
- Masoud Babaie-Zadeh ,
- Christian Jutten
IEEE International Conference on Machine Learning for Signal Processing (MLSP) |
Published by IEEE MLSP
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 4dB or more and also outperforms Extended Kalman Smoother filtering for about 2dB 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.