Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter

Fabio La Foresta, Nadia Mammone, Francesco Carlo Morabito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper a novel method, called WICA, based on the joint use of wavelet transform (WT) and independent component analysis (ICA) is discussed. The main advantage of this method is that it encompasses the characteristics of WT and ICA. In order to show the novelty of our method, we present a biomedical signal processing application in which ICA has poor performances, whereas WICA yields good results. In particular, we discuss the artifact cancellation in electrocardiographic (ECG) signals. The results show the ability of WICA to cancel some artifact from ECG when only two signals are recorded.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages78-82
Number of pages5
Volume3931 LNCS
Publication statusPublished - 2006
Event16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005 - Vietri sul Mare, Italy
Duration: Jun 8 2005Jun 11 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3931 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005
CountryItaly
CityVietri sul Mare
Period6/8/056/11/05

Fingerprint

Wavelet Analysis
Independent component analysis
Independent Component Analysis
Electrocardiography
Cancellation
Artifacts
Filter
Wavelet transforms
Wavelet Transform
Cancel
Signal Processing
Joints
Electrocardiogram

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

La Foresta, F., Mammone, N., & Morabito, F. C. (2006). Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3931 LNCS, pp. 78-82). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3931 LNCS).

Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter. / La Foresta, Fabio; Mammone, Nadia; Morabito, Francesco Carlo.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3931 LNCS 2006. p. 78-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3931 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

La Foresta, F, Mammone, N & Morabito, FC 2006, Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3931 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3931 LNCS, pp. 78-82, 16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005, Vietri sul Mare, Italy, 6/8/05.
La Foresta F, Mammone N, Morabito FC. Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3931 LNCS. 2006. p. 78-82. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
La Foresta, Fabio ; Mammone, Nadia ; Morabito, Francesco Carlo. / Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3931 LNCS 2006. pp. 78-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{e871e57b047e4bb6b2435992b6cff1ac,
title = "Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter",
abstract = "In this paper a novel method, called WICA, based on the joint use of wavelet transform (WT) and independent component analysis (ICA) is discussed. The main advantage of this method is that it encompasses the characteristics of WT and ICA. In order to show the novelty of our method, we present a biomedical signal processing application in which ICA has poor performances, whereas WICA yields good results. In particular, we discuss the artifact cancellation in electrocardiographic (ECG) signals. The results show the ability of WICA to cancel some artifact from ECG when only two signals are recorded.",
author = "{La Foresta}, Fabio and Nadia Mammone and Morabito, {Francesco Carlo}",
year = "2006",
language = "English",
isbn = "3540331832",
volume = "3931 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "78--82",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Artifact cancellation from electrocardiogram by mixed wavelet-ICA filter

AU - La Foresta, Fabio

AU - Mammone, Nadia

AU - Morabito, Francesco Carlo

PY - 2006

Y1 - 2006

N2 - In this paper a novel method, called WICA, based on the joint use of wavelet transform (WT) and independent component analysis (ICA) is discussed. The main advantage of this method is that it encompasses the characteristics of WT and ICA. In order to show the novelty of our method, we present a biomedical signal processing application in which ICA has poor performances, whereas WICA yields good results. In particular, we discuss the artifact cancellation in electrocardiographic (ECG) signals. The results show the ability of WICA to cancel some artifact from ECG when only two signals are recorded.

AB - In this paper a novel method, called WICA, based on the joint use of wavelet transform (WT) and independent component analysis (ICA) is discussed. The main advantage of this method is that it encompasses the characteristics of WT and ICA. In order to show the novelty of our method, we present a biomedical signal processing application in which ICA has poor performances, whereas WICA yields good results. In particular, we discuss the artifact cancellation in electrocardiographic (ECG) signals. The results show the ability of WICA to cancel some artifact from ECG when only two signals are recorded.

UR - http://www.scopus.com/inward/record.url?scp=33745769529&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745769529&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540331832

SN - 9783540331834

VL - 3931 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 78

EP - 82

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -