Key result
Novel RVFLN model outperforms existing methods for ECG artifact removal, achieving ~68 dB SNR.
Why the study?
Removing ECG artifacts from EEG signals is essential to extract clean clinical information, especially in cardiac patients, but effective artifact removal methods are needed.
A novel machine learning approach using RVFLN and RLS algorithms effectively removes ECG artifacts from EEG signals.
May enhance EEG quality amid cardiac artifacts; leaves open prospective validation before clinical adoption.
Electroencephalography (EEG), helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range. To extract clean clinical information from EEG signals, it is essential to remove unwanted artifacts that are due to different causes including at the time of acquisition. In this piece of work, the authors considered the EEG signal contaminated with Electrocardiogram (ECG) artifacts that occurs mostly in cardiac patients. The clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in the EEG signal and verify the proposed algorithm. Being the artifactual signal is non-linear and non-stationary the Random Vector Functional Link Network (RVFLN) model is used in this case. The Machine Learning approach has taken a leading role in every field of current research and RVFLN is one of them. For the proof of adaptive nature, the model is designed with EEG as a reference and artifactual EEG as input. The peaks of ECG signals are evaluated for artifact estimation as the amplitude is higher than the EEG signal. To vary the weight and reduce the error, an exponentially weighted Recursive Least Square(RLS) algorithm is used to design the adaptive filter with the novel RVFLN model. The random vectors are considered in this model with a radial basis function to satisfy the required signal experimentation. It is found that the result is excellent in terms of Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Relative Error (RE), Gain in Signal to Artifact Ratio (GSAR), Signal Noise Ratio(SNR), Information Quantity (IQ), and Improvement in Normalized Power Spectrum (INPS). Also, the proposed method is compared with the earlier methods to show its efficacy.
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Behera et al. (2022) studied ECG artifacts in EEG signals (n=27). Random Vector Functional Link Network (RVFLN) model vs. Existing artifact removal algorithms (e.g., RLS notch filter, ICA, Infomax) was evaluated on Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The proposed Random Vector Functional Link Network (RVFLN) model effectively removed ECG artifacts from EEG signals, achieving a Signal to Noise Ratio of 68.11 dB and a Mean Square Error of 0.1998 μV2, outperforming existing methods.
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