title: A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet.
abstract: In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97dB and 0.02 respectively and a significantly higher correlation coefficient (p<0.05).
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, 721 23, Högskoleplan 1, Västerås, Sweden. Electronic address: sara.x.abbaspour@mdh.se.
authors.forename: Sara
authors.initials: S
authors.lastname: Abbaspour
authors.affiliation: Department of Biomedical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Fallah
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, 721 23, Högskoleplan 1, Västerås, Sweden.
authors.forename: Maria
authors.initials: M
authors.lastname: Lindén
authors.affiliation: School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, 1142 Auckland, New Zealand.
authors.forename: Hamid
authors.initials: H
authors.lastname: Gholamhosseini
mesh_terms: D004562:Electrocardiography / Q000379:methods / Q000592:standards*; D004576:Electromyography / Q000379:methods / Q000592:standards*; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D016571:Neural Networks, Computer; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis; D055815:Young Adult
keywords: ANFIS; ECG interference; EMG signal; Noise removal; Wavelet