Key result
A recursive least square notch filter effectively suppressed ECG artifacts from EEG recordings, achieving significantly higher improvement of normalized power spectrum than ICA and Infomax methods.
Why the study?
EEG is often contaminated with artifacts that complicate data interpretation, requiring effective methods to suppress ECG artifacts from EEG recordings.
The RLS notch filter effectively eliminates ECG artifacts from EEG signals while preserving underlying EEG information better than ICA and Infomax methods.
Answers are grounded in this paper and cite their sources.
Promising in animals for ECG-EEG artifact removal; leaves open human validation before any clinical adoption.
Electroencephalogram (EEG) is a common tool for medical diagnosis, cognitive research, and managing neurological disorders. However, EEG is usually contaminated with various artifacts, making it difficult to interpret EEG data. In this study, a recursive least square (RLS) notch filter was developed to effectively suppress electrocardiogram (ECG) artifacts from EEG recordings. ECG artifacts were estimated and modeled using the instantaneous frequency of the cardiac cycle. Then it was adaptively estimated using a RLS filter and directly subtracted from contaminated EEG data. Based on the validation measures of improvement of normalized power spectrum (INPS), mean square error (MSE) and information quantity (IQ), the performance of ECG artifacts suppression was compared among the proposed RLS approach, independent component analysis (ICA) and blind deconvolution method under information maximization (Infomax) on simulated and animal experimental data. Simulation data demonstrated that INPS of RLS method (19.75(18.37,20.95) dB) was significantly higher than that of ICA (4.35(3.35,5.41) dB) and Infomax (5.76(4.60,6.88) dB). Meanwhile, MSE of RLS method (0.20(0.08,0.53) μV 2 ) was considerably lower than that of ICA (5.59(2.35,19.79) μV 2 ) and Infomax (3.21(1.01,10.69) μV 2 ). Animal experimental data showed that INPS was 1.76(0.42,9.40) dB for RLS method, which was dramatically higher than that of ICA (0.02(0.00,0.14) dB) and Infomax (0.57(0.08,2.45) dB). The calculated IQ for RLS method (0.331(0.021,0.584)) was relatively lower than that of raw EEG (0.350(0.070,0.586)), ICA (0.350(0.069,0.581)) and Infomax (0.341(0.050,0.585)). The RLS notch filter can effectively eliminate ECG artifacts from EEG and preserve the majority of EEG information with little loss.
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Dai et al. (2019) studied ECG artifacts in EEG recordings. Recursive least square (RLS) notch filter vs. Independent component analysis (ICA) and Infomax was evaluated on Improvement of normalized power spectrum (INPS) on simulated data. A recursive least square notch filter effectively suppressed ECG artifacts from EEG recordings, achieving significantly higher improvement of normalized power spectrum than ICA and Infomax methods.
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