title: Classification of surface EMG signals using harmonic wavelet packet transform.
abstract: In this paper, an efficient method based on the discrete harmonic wavelet packet transform (DHWPT) is presented to classify surface electromyographic (SEMG) signals. After the relative energy of SEMG signals in each frequency band had been extracted by the DHWPT, a genetic algorithm was utilized to select appropriate features in order to reduce the feature dimensionality. Then, the selected features were used as the input vectors to a neural network classifier to discriminate four types of prosthesis movements. Compared with other classification methods, the proposed method provided high classification accuracy in experimental research. In addition, this method could also save a lot of computational time because the DHWPT has a fast algorithm based on the fast Fourier transform for numerical implementation.
authors.affiliation: Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China. wgnick@gmail.com
authors.forename: Gang
authors.initials: G
authors.lastname: Wang
authors.forename: Zhiguo
authors.initials: Z
authors.lastname: Yan
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
authors.forename: Hongbo
authors.initials: H
authors.lastname: Xie
authors.forename: Zhizhong
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000818:Animals; D002417:Cattle; D003627:Data Interpretation, Statistical; D017097:Electric Impedance; D004566:Electrodes; D066298:In Vitro Techniques; D018482:Muscle, Skeletal / Q000503:physiopathology*; D009213:Myography; D009468:Neuromuscular Diseases / Q000503:physiopathology*; D013696:Temperature