Key result
The proposed fully automatic ECG artefact removal method using Independent Component Analysis achieved an average sensitivity of 100% and specificity of 99.94% on high-density resting state EEG data.
Why the study?
Removal of cardiac artefacts from EEG signals normally requires manual inspection or reference channels like ECG, motivating an automatic removal method without a reference channel.
A novel, fully automatic ICA-based method effectively detects and removes ECG artefacts from EEG signals without requiring a reference ECG channel, achieving >99% sensitivity.
Hypothesis-generating for reference-free ECG artefact removal in EEG; prospective validation needed before clinical use.
Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or the use of reference channels (EOG, EMG, ECG). We present a novel, fully automatic method for the detection and removal of ECG artefacts that works without a reference ECG channel. Independent Component Analysis (ICA) is applied to the measured data and the independent components are examined for the presence of QRS waveforms using an adaptive threshold-based QRS detection algorithm. Detected peaks are subsequently classified by a rule-based classifier as ECG or non-ECG components. Components manifesting ECG activity are marked for removal, and then the artefact-free signal is reconstructed by removing these components before performing the inverse ICA. The performance of the proposed method is evaluated on a number of EEG datasets and compared to results reported in the literature. The average sensitivity of our ECG artefact removal method is above 99 %, which is better than known literature results.
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Issa et al. (2019) studied Healthy volunteers and patients with sleep disorders or arrhythmias (n=61). Automatic ECG artefact removal method using ICA and QRS detection vs. Literature methods was evaluated on Sensitivity of ECG component classifier. The proposed fully automatic ECG artefact removal method using Independent Component Analysis achieved an average sensitivity of 100% and specificity of 99.94% on high-density resting state EEG data.
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