Wavelet Based Dictionaries for Dimensionality Reduction of ECG Signals

Dimensionality reduction of ECG signals is considered within the framework of sparse representation. The approach constructs the signal model by selecting elementary components from a redundant wavelet dictionary via a greedy strategy. The technical details and algorithms for constructing the distionaries are given in a complemenaty work. The reduced representation of the signal is shown to be suitable for compression at low level distortion. In that regard, compression results are superior to previously reported benchmarks on the MIT-BIH Arrhythmia data set.

"Wavelet Based Dictionaries for Dimensionality Reduction of ECG Signals"
by Laura Rebollo-Neira and Dana Cerna

The routines for reproducing the results of the paper are available here. Note: the file is very large because it contains 48 ECG records. A smaller file for the construction of the wavelet dictionaries can be found here.

Highly Nonlinear Approximations for Sparse Signal Representation

Wavelet Based Dictionaries for Dimensionality Reduction of ECG Signals