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  • Research Article
  • Open Access

Cancelling ECG Artifacts in EEG Using a Modified Independent Component Analysis Approach

  • 1Email author,
  • 1,
  • 2,
  • 2 and
  • 3
EURASIP Journal on Advances in Signal Processing20082008:747325

  • Received: 3 April 2008
  • Accepted: 31 July 2008
  • Published:


We introduce a new automatic method to eliminate electrocardiogram (ECG) noise in an electroencephalogram (EEG) or electrooculogram (EOG). It is based on a modification of the independent component analysis (ICA) algorithm which gives promising results while using only a single-channel electroencephalogram (or electrooculogram) and the ECG. To check the effectiveness of our approach, we compared it with other methods, that is, ensemble average subtraction (EAS) and adaptive filtering (AF). Tests were carried out on simulated data obtained by addition of a filtered ECG on a visually clean original EEG and on real data made up of 10 excerpts of polysomnographic (PSG) sleep recordings containing ECG artifacts and other typical artifacts (e.g., movement, sweat, respiration, etc.). We found that our modified ICA algorithm had the most promising performance on simulated data since it presented the minimal root mean-squared error. Furthermore, using real data, we noted that this algorithm was the most robust to various waveforms of cardiac interference and to the presence of other artifacts, with a correction rate of 91.0%, against 83.5% for EAS and 83.1% for AF.


  • Respiration
  • Simulated Data
  • Independent Component Analysis
  • Correction Rate
  • Independent Component Analysis

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Authors’ Affiliations

TCTS Lab, Faculté Polytechnique de Mons, 31 Boulevard Dolez, 7000 Mons, Belgium
Sleep Laboratory, CHU de Charleroi, Vésale Hospital, Université Libre de Bruxelles, Rue de Gozée 706, 6110 Montigny-le-Tilleul, Belgium
Computer Engineering Department, CHU Tivoli Hospital, 7100 La Louvière, Belgium


© Stéphanie Devuyst et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.