Adaptive filter theory
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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.