Removal of ECG Artifacts From EEG Using an Effective Recursive Least Square Notch Filter

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.