Contaminated ECG Artifact Detection and Elimination from EEG Using Energy Function Based Transformation

Abstract

Electrical field of the heart (ECG) propagates throughout the body and introduce artifact in EEG recordings which may lead to incorrect interpretation of monitoring result. Hence in this paper, we present a method of automatic detection and reduction of ECG artifact from EEG. ECG has its own spike like property and periodicity. Moreover, it also has lack of correlation with the EEG signal. We have utilized the aforementioned properties to detect ECG artifact in EEG and have employed a method to remove it automatically. In the first step of the algorithm, an energy function based method is used to emphasize the R-waves of contaminated ECG artifact and thereafter, an adaptive thresholding method along with clustering is used to detect contaminated candidate R-spikes of ECG artifact in EEG signal. After that utilizing periodic information of R-wave, a searching mechanism is employed as post processing to detect the R-peaks more accurately. Thereafter, noise model of ECG artifact contaminated with EEG is generated and finally it is subtracted from the EEG recordings to decontaminate it from the artifact. Before subtraction, a time varying alignment procedure is applied to increase the effectiveness of the artifact reduction method. Results obtained from our extensive experiments show that the proposed method is effective and encouraging in terms of automatic ECG artifact detection and reduction from EEG signal.