title: Evolutionary computing based approach for the removal of ECG artifact from the corrupted EEG signal.
abstract: BACKGROUND
  Electroencephalogram (EEG) is an important tool for clinical diagnosis of brain-related disorders and problems. However, it is corrupted by various biological artifacts, of which ECG is one among them that reduces the clinical importance of EEG especially for epileptic patients and patients with short neck.
  
  
  OBJECTIVE
  To remove the ECG artifact from the measured EEG signal using an evolutionary computing approach based on the concept of Hybrid Adaptive Neuro-Fuzzy Inference System, which helps the Neurologists in the diagnosis and follow-up of encephalopathy.
  
  
  METHODS
  The proposed hybrid learning methods are ANFIS-MA and ANFIS-GA, which uses Memetic Algorithm (MA) and Genetic algorithm (GA) for tuning the antecedent and consequent part of the ANFIS structure individually. The performances of the proposed methods are compared with that of ANFIS and adaptive Recursive Least Squares (RLS) filtering algorithm.
  
  
  RESULTS
  The proposed methods are experimentally validated by applying it to the simulated data sets, subjected to non-linearity condition and real polysomonograph data sets. Performance metrics such as sensitivity, specificity and accuracy of the proposed method ANFIS-MA, in terms of correction rate are found to be 93.8%, 100% and 99% respectively, which is better than current state-of-the-art approaches.
  
  
  CONCLUSIONS
  The evaluation process used and demonstrated effectiveness of the proposed method proves that ANFIS-MA is more effective in suppressing ECG artifacts from the corrupted EEG signals than ANFIS-GA, ANFIS and RLS algorithm.
authors.affiliation: Department of Electronics and Communication Engineering, Regional Centre, Anna University, Tirunelveli Region, Tirunelveli, Tamil Nadu, India.
authors.forename: S Suja
authors.initials: SS
authors.lastname: Priyadharsini
authors.affiliation: Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India.
authors.forename: S Edward
authors.initials: SE
authors.lastname: Rajan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003951:Diagnostic Errors / Q000517:prevention & control*; D004562:Electrocardiography; D004569:Electroencephalography / Q000592:standards*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D012984:Software
keywords: Adaptive Neuro-Fuzzy Inference System; Hybrid-ANFIS; electrocardiogram; electroencephalogram; genetic algorithm; memetic algorithm