Detection of muscle artifact epochs using entropy based M-DDTW technique in EEG signals

Abstract

The exclusion of artifacts plays an indispensable role in the processing of Electroencephalographic (EEG) recordings. This work highlights one such inescapable artifactual event known as muscle artifacts (MA) and its detection methodology. These are high-frequency signals that are recurrently present in EEG and generally recorded via Electromyogram. The paper presents an entropy based Manhattan derivative dynamic time warping (M-DDTW) technique for MA epoch detection. Manhattan (City block) and Canberra distance have been proposed as the distance to be optimized by using them with dynamic time warping (DTW) and derivative dynamic time warping (DDTW) technique. The study reduces the computational time and improves the performance by utilizing entropy for reference generation and identifies the optimal threshold value for each technique. The results for the optimal threshold have been validated on the real EEG dataset. It was observed that the proposed entropy based M-DDTW technique exhibits the highest performance of 90 % and an accuracy of 95 % at an optimal threshold surpassing state of the art techniques. The testing of qualitative performance and time consumption has been done using traditional mode decomposition methods. The proposed Entropy based M-DDTW technique along with EEMD showed a noteworthy performance compared to other techniques. Overall the combination of entropy with time warping based local distance variation appears to be an adequate solution for muscle artifact detection.