  This paper addresses compressive sensing for multi-channel ECG. Compared to
the traditional sparse signal recovery approach which decomposes the signal
into the product of a dictionary and a sparse vector, the recently developed
cosparse approach exploits sparsity of the product of an analysis matrix and
the original signal. We apply the cosparse Greedy Analysis Pursuit (GAP)
algorithm for compressive sensing of ECG signals. Moreover, to reduce
processing time, classical signal-channel GAP is generalized to the
multi-channel GAP algorithm, which simultaneously reconstructs multiple signals
with similar support. Numerical experiments show that the proposed method
outperforms the classical sparse multi-channel greedy algorithms in terms of
accuracy and the single-channel cosparse approach in terms of processing speed.
