A Clustering Approach to Construct Multi-scale Overcomplete Dictionaries for ECG Modeling | IEEE Conference Publication | IEEE Xplore

A Clustering Approach to Construct Multi-scale Overcomplete Dictionaries for ECG Modeling


Abstract:

The electrocardiogram (ECG) is the main biomedical signal used to diagnose and monitor cardiac pathologies. A typical ECG is composed of quasi-periodic activations (the Q...Show More

Abstract:

The electrocardiogram (ECG) is the main biomedical signal used to diagnose and monitor cardiac pathologies. A typical ECG is composed of quasi-periodic activations (the QRS complexes, and the P and T waves) and periods of inactivity, plus noise and interferences. The sparse nature of the ECG has lead to the development of many compressed sensing (CS) and sparsity-aware ECG signal processing algorithms. In order to attain a good performance, these methods require appropriate dictionaries, and several on-line dictionary construction approaches have been devised. However, all of them require a substantial computational cost and the derived dictionaries are composed of atoms which may not be representative of real-world signals. In this work, we describe an efficient method for off-line construction of an overcomplete and multi-scale dictionary using a clustering-based approach. The resulting dictionary, whose atoms are the most representative waveforms from the training set, is then used to obtain a sparse representation of the ECG signal. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.
Date of Conference: 12-17 May 2019
Date Added to IEEE Xplore: 16 April 2019
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Conference Location: Brighton, UK

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