Comput. Biol. Med. - Ant colony optimization-based feature selection method for surface electromyography signals classification.

Tópicos

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Resumo

This paper presented a new ant colony optimization (ACO) feature selection method to classify hand motion surface electromyography (sEMG) signals. The multiple channels of sEMG recordings make the dimensionality of sEMG feature grow dramatically. It is known that the informative feature subset with small size is a precondition for the accurate and computationally efficient classification strategy. Therefore, this study proposed an ACO based feature selection scheme using the heuristic information measured by the minimum redundancy maximum relevance criterion (ACO-mRMR). The experiments were conducted on ten subjects with eight upper limb motions. Two feature sets, i.e., time domain features combined with autoregressive model coefficients (TDAR) and wavelet transform (WT) features, were extracted from the recorded sEMG signals. The average classification accuracies of using ACO reduced TDAR and WT features were 95.45?2.2% and 96.08?3.3%, respectively. The principal component analysis (PCA) was also conducted on the same data sets for comparison. The average classification accuracies of using PCA reduced TDAR and WT features were 91.51?4.9% and 89.87?4.4%, respectively. The results demonstrated that the proposed ACO-mRMR based feature selection method can achieve considerably high classification rates in sEMG motion classification task and be applicable to other biomedical signals pattern analysis.

Resumo Limpo

paper present new ant coloni optim aco featur select method classifi hand motion surfac electromyographi semg signal multipl channel semg record make dimension semg featur grow dramat known inform featur subset small size precondit accur comput effici classif strategi therefor studi propos aco base featur select scheme use heurist inform measur minimum redund maximum relev criterion acomrmr experi conduct ten subject eight upper limb motion two featur set ie time domain featur combin autoregress model coeffici tdar wavelet transform wt featur extract record semg signal averag classif accuraci use aco reduc tdar wt featur respect princip compon analysi pca also conduct data set comparison averag classif accuraci use pca reduc tdar wt featur respect result demonstr propos acomrmr base featur select method can achiev consider high classif rate semg motion classif task applic biomed signal pattern analysi

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