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Báo cáo hóa học: " Research Article Cancelling ECG Artifacts in EEG Using a Modified Independent Component Analysis Approach"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Cancelling ECG Artifacts in EEG Using a Modified Independent Component Analysis Approach | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 747325 13 pages doi 10.1155 2008 747325 Research Article Cancelling ECG Artifacts in EEG Using a Modified Independent Component Analysis Approach Stephanie Devuyst 1 Thierry Dutoit 1 Patricia Stenuit 2 Myriam Kerkhofs 2 and Etienne Stanus3 1 TCTS Lab Faculte Polytechnique de Mons 31 Boulevard Dolez 7000 Mons Belgium 2 Sleep Laboratory CHU de Charleroi Vesale Hospital Universite Libre de Bruxelles Rue de Gozee 706 6110 Montigny-le-Tilleul Belgium 3 Computer Engineering Department CHU Tivoli Hospital 7100LaLouviere Belgium Correspondence should be addressed to Stephanie Devuyst stephanie.devuyst@fpms.ac.be Received 3 April 2008 Revised 11 July 2008 Accepted 31 July 2008 Recommended by Kenneth Barner 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. Copyright 2008 Stephanie Devuyst et al. .