Abstract 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.

中文翻译：

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从 EEG 信号中自动去除 ECG 伪影

摘要 脑电图 (EEG) 信号经常受到眼部、肌肉和心脏伪影的污染，这些伪影的去除通常需要人工检查或使用参考通道（EOG、EMG、ECG）。我们提出了一种新颖的全自动方法，用于检测和去除 ECG 伪影，该方法无需参考 ECG 通道即可工作。独立分量分析 (ICA) 应用于测量数据，并使用基于自适应阈值的 QRS 检测算法检查独立分量是否存在 QRS 波形。检测到的峰值随后由基于规则的分类器分类为 ECG 或非 ECG 组件。表现出 ECG 活动的组件被标记为移除，然后通过在执行反向 ICA 之前移除这些组件来重建无伪影信号。所提出方法的性能在许多 EEG 数据集上进行了评估，并与文献中报告的结果进行了比较。我们的 ECG 伪影去除方法的平均灵敏度在 99% 以上，优于已知的文献结果。