Removal of Artifacts from Electroenchaphalography Signal using Multiwavelet Transform

Abstract

The signal from the brain can be recorded using Electroenchaphalography (EEG). The proposed work summarizes a unique method which is used for the removal of mixed artifacts presented in the electroencephalography signal during the acquisition process. Artifacts comprises of various bio-potential unit such as electrooculogram, electrocardiogram, and electromyogram. These artifacts are referred as a noise sources which is responsible for the complexity of the EEG signal. The artifacts obtained from the EEG signal leads towards improper diagnosis of pathological conditions. The EEG signal which is obtained from the brain is the multi-dimensional signal with the various statistical properties. Time consumption of the EEG signal is not reproducible due to the biological properties of the signal. The information of the EEG signal consists of the data of the neuron levels which is collected for every millisecond with the temporal resolution scale. In account of special cases, EEG signal contains noise and artifacts where information is collected using the extraction of signals. To obtain the information of the artifacts the proposed technique is used to maintain higher accuracy in the extraction process. The proposed technique consists of multiwavelet transform to remove the artifacts from the input EEG signal. In the proposed multiwavelet transform, the signal which consists of noisy features can be decomposed using GHM and thresholding technique. This experimental analysis shows the removal of artifacts from the EEG signals. The pathological conditions are removed which leads to the increase in the accuracy of the system. Also, this research findings shows that the proposed multiwavelet transform based approach outperforms significantly with respect to conventional approaches. The reconstructed EEG signal has the lesser reliability range which is measured in-terms of signal to noise ratio and power spectral density.