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

Chenxi Dai,Jianjie Wang,Yongqin Li,Weiming Li,Jialing Xie,Yushun Gong

doi:10.1109/access.2019.2949842

Chenxi Dai, Jianjie Wang + Show 4 more

Open Access

https://doi.org/10.1109/access.2019.2949842

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Abstract

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.

Highlights

Electroencephalogram (EEG) is an electrophysiological monitoring tool to record brain activity with the characteristics of convenient acquisition, noninvasive access, and high temporal resolution [1]
Strong ECG artifacts were introduced and QRS-like spikes were observed in the corrupted EEG signal
This study introduces a new ECG artifacts cancellation method based on an artifact model that needs the instantaneous frequency of the cardiac cycle as additional information, and only a single-channel EEG and ECG were used

Summary

Introduction

Electroencephalogram (EEG) is an electrophysiological monitoring tool to record brain activity with the characteristics of convenient acquisition, noninvasive access, and high temporal resolution [1]. Interfering signals from technical or physiological sources firstly make the analysis and interpretation of EEG signals difficult, corrupt the. Quantitative EEG results, and eventually affect the diagnosis of cortical activity [2]. Electrocardiogram (ECG) artifacts, even below the visible level, have been shown to significantly degrade the quality of quantitative EEG measures [4], [5]. Cardiac interference recorded by EEG electrodes often presents spiky, quasi-periodic signals, which can seriously affect EEG basic rhythm waves (0-30Hz) [6]. It is a huge challenge to analyze and interpret brain activity under ECG artifacts in practices

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