Performance analysis of swarm intelligence algorithms in removal of ecg artefact from tainted eeg signal

Abstract

Electroencephalogram (EEG) is the recording of electrical activities of the brain. It is contaminated by other biological signals, known as artefacts. In this research paper, the performance analysis of three swarm intelligence incorporated adaptive neuro fuzzy inference system (ANFIS)-based techniques is made with respect to ECG artefact removal from the corrupted EEG signal. Swarm intelligence algorithms such as improved artificial immune system (IAIS), artificial immune system (AIS) and particle swarm optimization (PSO) are employed for artefact removal, by tuning the parameters of ANFIS individually. The performances of the methods are experimentally validated for both simulated and real data sets. Measures such as signal to noise ratio (SNR), mean square error (MSE) value, correlation coefficient, power spectrum density plot, sensitivity, specificity and accuracy are used for analysing the performance of the methods of simulated data set. The sensitivity, specificity and accuracy of ANFIS-tuned IAIS (ANFIS-IAIS), are found to be 94.9%, 100% and 99.2%, respectively The sensitivity, specificity and accuracy of ANFIS-AIS and ANFIS-PSO are 91.9%, 100%, 98.7% and 87.9%, 100%, 98.3%, respectively. From the results, it is found that ANFIS-IAIS is more effective in removing ECG artefacts from EEG signals than ANFIS-AIS and ANFIS-PSO.