<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Automatic ECG Artefact Removal from EEG Signals

Department of Electrical Engineering and Information Systems, Faculty of Information Technology

Department of Scientific Computing, Faculty of Computers and Informatics

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Automatic ECG Artefact Removal from EEG Signals"}

Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or...

Automatic ECG Artefact Removal from EEG Signals

Data publikacji: 26 cze 2019

Zakres stron: 101 - 108

Otrzymano: 17 gru 2018

Przyjęty: 30 maj 2019

DOI: https://doi.org/10.2478/msr-2019-0016

Słowa kluczowe
EEG, ECG artefact removal, Independent Component Analysis, QRS detection, cardiac cycle classification

© 2019 Mohamed F. Issa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic ECG Artefact Removal from EEG Signals

Data publikacji: 26 cze 2019

Zakres stron: 101 - 108

Otrzymano: 17 gru 2018

Przyjęty: 30 maj 2019

DOI: https://doi.org/10.2478/msr-2019-0016

Słowa kluczoweEEG, ECG artefact removal, Independent Component Analysis, QRS detection, cardiac cycle classification

© 2019 Mohamed F. Issa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
EEG, ECG artefact removal, Independent Component Analysis, QRS detection, cardiac cycle classification