Key result
Wavelet-based dictionaries provided superior dimensionality reduction and compression of ECG signals at low distortion levels compared to standard wavelet bases.
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The proposed wavelet-based dictionary approach provides superior dimensionality reduction and compression of ECG signals compared to standard wavelet bases and existing benchmarks.
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 dictionary via a greedy strategy. The proposed wavelet dictionaries are built from the multiresolution scheme, but translating the prototypes within a shorter step than that corresponding to the wavelet basis. 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.
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Rebollo‐Neira et al. (2018) studied Arrhythmia (ECG signals) (n=48). Wavelet Based Dictionaries vs. Wavelet bases was evaluated on Dimensionality reduction (Sparsity Ratio) and Compression Ratio. Wavelet-based dictionaries provided superior dimensionality reduction and compression of ECG signals at low distortion levels compared to standard wavelet bases.
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