Wavelet based dictionaries for dimensionality reduction of ECG signals. (September 2019)

• Record Type: Journal Article
• Authors: Rebollo-Neira, Laura; Černá, Dana
• Is Part Of: Biomedical signal processing and control. Volume 54(2019)
• Publication Date: 2019-09
• Subjects: Sparse representation -- ECG compression -- Wavelet dictionaries -- Greedy pursuit strategies; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101593
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A method for piecewise modelling of ECG signals is proposed. The model represents the signal as superposition of elementary components selected from a wavelet dictionary. The reduced representation of the signal renders high compression. A metric of local sparsity is shown to be relevant to the detection of non-stationary noise or significant distortion in patterns. MATLAB software for reproducing the results in the paper has been made available on a dedicated website. Abstract: 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.