| Autor: |
Issa Mohamed F., Tuboly Gergely, Kozmann György, Juhasz Zoltan |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Measurement Science Review, Vol 19, Iss 3, Pp 101-108 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
1335-8871 |
| DOI: |
10.2478/msr-2019-0016 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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