  Multilead ECG compression (MlEC) has attracted tremendous attention in
long-term monitoring of the patients heart behavior. This paper proposes a
method denoted by block sparse MlEC (BlS MlEC) in order to exploit between-lead
correlations to compress the signals in a more efficient way. This is due to
the fact that multi-lead ECG signals are multiple observations of the same
source (heart) from different locations. Consequently, they have high
correlation in terms of the support set of their sparse models which leads them
to share dominant common structure. In order to obtain the block sparse model,
the collaborative version of lasso estimator is applied. In addition, we have
shown that raised cosine kernel has advantages over conventional Gaussian and
wavelet (Daubechies family) due to its specific properties. It is demonstrated
that using raised cosine kernel in constructing the sparsifying basis matrix
gives a sparser model which results in higher compression ratio and lower
reconstruction error. The simulation results show the average improvement of
37%, 88% and 90-97% for BlS M-lEC compared to the non-collaborative case with
raised cosine kernel, Gaussian kernel and collaborative case with Daubechies
wavelet kernels, respectively, in terms of reconstruction error while the
compression ratio is considered fixed.
