Efficient detection and correction of variable strength ECG artifact from single channel EEG

Abstract

The electroencephalogram (EEG) electrodes are susceptible to electrocardiogram (ECG) artifacts, misleading the analysis and diagnosis from the EEG recording. This paper mainly focuses on detection and correction of contaminating ECG artifact of various strengths, from a single channel EEG in the absence of a reference ECG channel. The algorithm involves two stages i.e. detection of ECG artifacts and correction of those artifacts. For detection of ECG artifact, modified S-transform (MST) is used on the bandpass filtered contaminated EEG to localize the higher energy QRS segments in time scale. For improved energy concentration of MST around the instantaneous frequency, artifact detection proximity in time scale is.05s. To correct ECG artifacts from EEG, a modified ensembled average subtraction is proposed, which restrict the overcompensation of the EEG in the correction process. The proposed algorithm is tested on MIT/BIH Polysomnography dataset and synthetic data set with various contamination strength, together more than 45 hours of EEG data. The proposed method achieves mean positive predictive value (PPV) and failed detection rate (FDR) of 97.87% and 2.13% respectively for MIT/BIH Polysomnography EEG recordings. The mean PPV and FDR for the CAP sleep EEG recordings with various contamination levels of ECG are found to be 98.77% and 1.22% respectively. The proposed algorithm is compared against existing Continuous wavelet transform (CWT) and Empirical ensembled mode decomposition (EEMD) based algorithms where the proposed algorithm found to be performing better in terms of spike to energy ratio (SER), spike to background energy ratio (SBR), correlation factors, Mutual information (MI), Mean absolute error (MAE) and Mean square error (MSE) along with a lower change in power spectral density (ΔPSD) in various brain frequency bands.