An ELM Based Regression Model for ECG Artifact Minimization from Single Channel EEG

Abstract

Electroencephalogram (EEG) is the most widely used non-invasive technique to record the electrical activity of brain for analysis or diagnostic procedures. The sensitive electrodes of EEG are susceptible to high amplitude electrocardiogram (ECG) signals, which superimpose on the recorded EEG. Minimizing this artifact effectively from a single channel EEG without a reference ECG channel is a challenge. In this paper, extreme learning machine (ELM) algorithm as a regression model is implemented for ECG artifact removal from single channel EEG. The S-transform (ST) of the EEG signals are used as the feature set of for ELM training and testing. ST combines the progressive resolution and absolutely referenced phase information for the given time series uniquely. By training the ELM with pairs of contaminated and clean EEG signals both in magnitude and phase, is able to minimize the ECG artifact from contaminated EEG signal effectively in the testing phase. The average Root mean square error (RMSE) and the correlation coefficient (CC) for actual EEG signal to the estimated EEG signal from the ELM based regression model obtained are 0.32 and 0.96 respectively.