Descripción
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The electrocardiogram (ECG) was the ?rst biomedical signal where digital signal processing techniques were extensively applied. The ECG is a sparse signal, composed of relevant activations, periods of inactivity, noise and interferences. In this work, we describe an efficient method to construct overcomplete and multi-scale dictionaries for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods, the proposed approach learns the dictionary ?first, and then applies an efficient sparse inference algorithm to model the signal using the constructed dictionary. As a result, our method is able to deal with long recordings from multiple patients. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach. | |
Internacional
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Si |
Nombre congreso
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17th International Conference on Computer Aided Systems Theory |
Tipo de participación
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960 |
Lugar del congreso
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Revisores
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Si |
ISBN o ISSN
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0302-9743 |
DOI
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Fecha inicio congreso
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17/02/2019 |
Fecha fin congreso
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22/02/2019 |
Desde la página
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1 |
Hasta la página
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2 |
Título de las actas
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Proceedings of the 17th International Conference on Computer Aided Systems Theory |