Overcomplete Multi-Scale Dictionaries for Efficient Representation of ECG Signals

Meltzer, David and Luengo García, David and Trigano, Tom (2019). Overcomplete Multi-Scale Dictionaries for Efficient Representation of ECG Signals. In: "17th International Conference on Computer Aided Systems Theory_EUROCAST 2019", 17 al 22 de febrero de 2019, Las Palmas de Gran Canaria (España). ISBN 978-3-030-45096-0. pp. 331-338. https://doi.org/10.1007/978-3-030-45096-0_41.

Description

Title: Overcomplete Multi-Scale Dictionaries for Efficient Representation of ECG Signals
Author/s:
  • Meltzer, David
  • Luengo García, David
  • Trigano, Tom
Item Type: Presentation at Congress or Conference (Article)
Event Title: 17th International Conference on Computer Aided Systems Theory_EUROCAST 2019
Event Dates: 17 al 22 de febrero de 2019
Event Location: Las Palmas de Gran Canaria (España)
Title of Book: Computer Aided Systems Theory – EUROCAST 2019. Lecture Notes in Computer Science
Date: 2019
ISBN: 978-3-030-45096-0
Volume: 12014
Subjects:
Freetext Keywords: electrocardiogram (ECG); LASSO; overcomplete multi-scale signal representation; dictionary construction; sparse inference
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

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (353kB) | Preview

Abstract

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.

More information

Item ID: 65125
DC Identifier: http://oa.upm.es/65125/
OAI Identifier: oai:oa.upm.es:65125
DOI: 10.1007/978-3-030-45096-0_41
Official URL: https://link.springer.com/chapter/10.1007/978-3-030-45096-0_41#citeas
Deposited by: Memoria Investigacion
Deposited on: 19 Feb 2021 10:35
Last Modified: 19 Feb 2021 10:35
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM