An Efficient method for the removal of ECG artifact from measured EEG Signal using PSO algorithm
نویسندگان
چکیده
Abstract Electroencephagram (EEG) is the recording of electrical activity of the brain. Though it is intended to record cerebral signals,it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better diagnosis. This paper proposes a hybrid learning algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for eliminating ECG artifact from EEG signal. The proposed hybrid learning algorithms, ANFIS-PSO uses Particle Swarm Optimization (PSO) algorithm for tuning the antecedent and consequent part of the ANFIS. Performance of the proposed technique is compared with ANFIS. Improvements in the output Signal-to-Noise Ratio (SNR) , minimum Mean-Square Error (MSE) along with the Power Spectrum Density (PSD) plot are used as the criteria for comparing the performance of the algorithm. It is found that the proposed ANFIS-PSO algorithm works better, and outperforms the ANFIS technique in minimizing the ECG artifacts from the corrupted EEG signals.
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