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

Luengo García, David and Meltzer, David (2019). A Clustering Approach to Construct Multi-scale Overcomplete Dictionaries for ECG Modeling. In: "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)", 12 al1 7 de mayo de 2019, Brighton (Reino Unido). pp. 1085-1089. https://doi.org/10.1109/ICASSP.2019.8682758..

Description

Title: A Clustering Approach to Construct Multi-scale Overcomplete Dictionaries for ECG Modeling
Author/s:
  • Luengo García, David
  • Meltzer, David
Item Type: Presentation at Congress or Conference (Article)
Event Title: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)
Event Dates: 12 al1 7 de mayo de 2019
Event Location: Brighton (Reino Unido)
Title of Book: Proceedings of ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing
Date: 2019
Subjects:
Freetext Keywords: ECG signal processing; sparse inference; off-line dictionary learning; hierarchical clustering; LASSO
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2015-64835-C3-3-RMIMOD-PLCUnspecifiedTransformadas trigonométricas discretas para comunicaciones MIMO por la red eléctrica

More information

Item ID: 65124
DC Identifier: http://oa.upm.es/65124/
OAI Identifier: oai:oa.upm.es:65124
DOI: 10.1109/ICASSP.2019.8682758.
Official URL: https://ieeexplore.ieee.org/document/8682758
Deposited by: Memoria Investigacion
Deposited on: 19 Feb 2021 10:53
Last Modified: 19 Feb 2021 11:10
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