  The purpose of sparse modelling of ECG signals is to represent an ECG record,
given by sample points, as a linear combination of as few elementary components
as possible. This can be achieved by creating a redundant set, called a
dictionary, from where the elementary components are selected. The success in
sparsely representing an ECG record depends on the nature of the dictionary
being considered. In this paper we focus on the construction of different
families of wavelet dictionaries, which are appropriate for the purpose of
reducing dimensionality of ECG signals through sparse representation modelling.
The suitability of wavelet dictionaries for ECG modelling, applying the
Optimized Orthogonal Matching Pursuit approach for the selection process, was
demonstrated in a previous work on the MIT-BIH Arrhythmia database consisting
of 48 records each of which of 30 min length. This paper complements the
previous one by presenting the technical details, methods, algorithms, and
MATLAB software facilitating the construction of different families of wavelet
dictionaries. The implementation allows for straightforward further extensions
to include additional wavelet families. The sparsity in the representation of
an ECG record significantly improves in relation to the sparsity produced by
the corresponding wavelet basis. This result holds true for the 17 wavelet
families considered here.
