1. bookVolume 19 (2019): Issue 3 (June 2019)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Open Access

Automatic ECG Artefact Removal from EEG Signals

Published Online: 26 Jun 2019
Volume & Issue: Volume 19 (2019) - Issue 3 (June 2019)
Page range: 101 - 108
Received: 17 Dec 2018
Accepted: 30 May 2019
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
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.

Keywords

[1] Dirlich, G., Vogl, L., Plaschke, M., Strian, F. (1997). Cardiac field effects on the EEG. Electroencephalography and Clinical Neurophysiology, 102 (4), 307-315.10.1016/S0013-4694(96)96506-2Search in Google Scholar

[2] Park, H.J., Jeong, D.U., Park, K.S. (2002). Automated detection and elimination of periodic ECG artefacts in EEG using the energy interval histogram method. IEEE Transactions on Biomedical Engineering, 49 (12), 1526-1533.10.1109/TBME.2002.805482Search in Google Scholar

[3] Suresh, H.N., Puttamadappa, C. (2008). Removal of EMG and ECG artifacts from EEG based on real time recurrent learning algorithm. International Journal of Physical Sciences, 3 (5), 120-125.Search in Google Scholar

[4] Correa, A.G., Laciar, E., Patiño, H.D., Valentinuzzi, M.E. (2007). Artefact removal from EEG signals using adaptive filters in cascade. Journal of Physics, 90 (1), 1-10.Search in Google Scholar

[5] Jutten, C., Herault, J. (1991). Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24 (1), 1-10.10.1016/0165-1684(91)90079-XSearch in Google Scholar

[6] Vigário, R., Sarela, J., Jousmiki, V., Hamalainen, M., Oja, E. (2000). Independent component approach to the analysis of EEG and MEG recordings. IEEE Transactions on Biomedical Engineering, 47 (5), 589-593.10.1109/10.84133010851802Search in Google Scholar

[7] Dora, C., Biswal, P.K. (2016). Robust ECG artefact removal from EEG using continuous wavelet transformation and linear regression. In International Conference on Signal Processing and Communications, Bangalore, India. IEEE, 1-5.Search in Google Scholar

[8] Jiang, J., Chao, C., Chiu, M., Lee, R., Tseng, C., Lin, R. (2007). An automatic analysis method for detecting and eliminating ECG artifacts in EEG. Computers in Biology and Medicine, 37 (11), 1660-1671.10.1016/j.compbiomed.2007.03.00717517386Search in Google Scholar

[9] Hamaneh, M.B., Chitravas, N., Kaiboriboon, K., Lhatoo, S.D., Loparo, K.A. (2014). Automated removal of EKG artefact from EEG data using independent component analysis and continuous wavelet transformation. IEEE Transactions on Biomedical Engineering, 61 (6), 1634-1641.10.1109/TBME.2013.229517324845273Search in Google Scholar

[10] Mak, J.N.F., Hu, Y., Luk, K.D.K. (2010). An automated ECG-artefact removal method for trunk muscle surface EMG recordings. Medical Engineering & Physics, 32, 840-848.10.1016/j.medengphy.2010.05.00720561810Search in Google Scholar

[11] Delorme, A., Makeig, S., Sejnowski, T.J. (2001). Automatic artefact rejection for EEG data using highorder statistics and independent component analysis. In 3rd International Independent Component Analysis and Blind Source Decomposition Conference, San Diego, 9-12.Search in Google Scholar

[12] Nolan, H., Whelan, R., Reilly, R.B. (2010). FASTER: Fully automated statistical thresholding for EEG artifact rejection. Journal of Neuroscience Methods, 192 (1), 152-162.10.1016/j.jneumeth.2010.07.01520654646Search in Google Scholar

[13] Mognon, A., Jovicich, J., Bruzzone, L., Buiatti, M. (2011). ADJUST: An automatic EEG artefact detector based on the joint use of spatial and temporal features. Psychophysiology, 48 (2), 229-240.10.1111/j.1469-8986.2010.01061.x20636297Search in Google Scholar

[14] Sahonero-Alvarez, G. (2017). A comparison of SOBI, FastICA, JADE and Infomax algorithms. In 8th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2017), Orlando, Florida, 17-22.Search in Google Scholar

[15] Rutledge, D.N., Jouan-Rimbaud Bouveresse, D. (2013). Independent components analysis with the JADE algorithm. TrAC Trends in Analytical Chemistry, 50, 22-32.10.1016/j.trac.2013.03.013Search in Google Scholar

[16] Shen, M., Zhang, X., Li, X. (2002). Independent component analysis of electroencephalographic signals. In 6th International Conference on Signal Processing, Beijing, China, 1548-1551.Search in Google Scholar

[17] Winkler, I., Haufe, S., Tangermann, M. (2011). Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behavioral and Brain Functions, 7, 30.10.1186/1744-9081-7-30Search in Google Scholar

[18] Burger, C., van den Heever, D.J. (2015). Removal of EOG artefacts by combining wavelet neural network and independent component analysis. Biomedical Signal Processing and Control, 15, 67-79.10.1016/j.bspc.2014.09.009Search in Google Scholar

[19] Ichimaru, Y., Moody, G.B. (1999). Development of the polysomnographic database on CD-ROM. Psychiatry and Clinical Neurosciences, 53, 175-177.10.1046/j.1440-1819.1999.00527.xSearch in Google Scholar

[20] Terzano, M.G., Parrino, L., Sherieri, A., et al. (2001). Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Medicine, 2 (6), 537-553.10.1016/S1389-9457(01)00149-6Search in Google Scholar

[21] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101 (23), 215-220.10.1161/01.CIR.101.23.e215Search in Google Scholar

[22] Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011, 1-9.10.1155/2011/156869302184021253357Search in Google Scholar

[23] Christov, I.I. (2004). Real time electrocardiogram QRS detection using combined adaptive threshold. Biomedical Engineering Online, 3 (1), 1-9.10.1186/1475-925X-3-2851678315333132Search in Google Scholar

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