Hybrid Binary Particle Swarm Optimization Differential Evolution-Based Feature Selection for EMG Signals Classification

Jingwei Too,Norhashimah Mohd Saad,Abdul Rahim Abdullah

doi:10.3390/axioms8030079

Jingwei Too, Norhashimah Mohd Saad + Show 1 more

Open Access

https://doi.org/10.3390/axioms8030079

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Journal: Axioms	Publication Date: Jul 5, 2019
Citations: 40	License type: CC BY 4.0

Affiliation: Technical University of Malaysia Malacca

Abstract

To date, the usage of electromyography (EMG) signals in myoelectric prosthetics allows patients to recover functional rehabilitation of their upper limbs. However, the increment in the number of EMG features has been shown to have a great impact on performance degradation. Therefore, feature selection is an essential step to enhance classification performance and reduce the complexity of the classifier. In this paper, a hybrid method, namely, binary particle swarm optimization differential evolution (BPSODE) was proposed to tackle feature selection problems in EMG signals classification. The performance of BPSODE was validated using the EMG signals of 10 healthy subjects acquired from a publicly accessible EMG database. First, discrete wavelet transform was applied to decompose the signals into wavelet coefficients. The features were then extracted from each coefficient and formed into the feature vector. Afterward, BPSODE was used to evaluate the most informative feature subset. To examine the effectiveness of the proposed method, four state-of-the-art feature selection methods were used for comparison. The parameters, including accuracy, feature selection ratio, precision, F-measure, and computation time were used for performance measurement. Our results showed that BPSODE was superior, in not only offering a high classification performance, but also in having the smallest feature size. From the empirical results, it can be inferred that BPSODE-based feature selection is useful for EMG signals classification.

Highlights

Electromyography (EMG) is a biomedical signal that records electric potential when there is a muscle contraction
In the proposed binary particle swarm optimization differential evolution (BPSODE), the binary particle swarm optimization (BPSO) and binary differential evolution (BDE) algorithms are computed in sequence, and no extra computation cost is required
In the second part of the experiment, we examined the efficacy of BPSODE by comparing its

Summary

Introduction

Electromyography (EMG) is a biomedical signal that records electric potential when there is a muscle contraction. The usefulness of EMG as a control source of myoelectric prosthetics has received much attention from biomedical researchers. The recognition of hand movements enables the application of multi-functional myoelectric prosthetics in engineering, rehabilitation, and clinical areas. Myoelectric control is still limited by inadequate control techniques [1]. EMG signals are influenced by noise due to the fact of its complex nature [2]. Most researchers apply advanced signal processing, feature extraction, and feature selection techniques to extract only the useful information from the signal

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