Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals
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Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
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Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
*
Authors to whom correspondence should be addressed.
Machines 2018, 6(4), 65; https://doi.org/10.3390/machines6040065
Received: 10 October 2018
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Revised: 7 December 2018
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Accepted: 11 December 2018
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Published: 13 December 2018
Electromyography (EMG) has been widely used in rehabilitation and myoelectric prosthetic applications. However, a recent increment in the number of EMG features has led to a high dimensional feature vector. This in turn will degrade the classification performance and increase the complexity of the recognition system. In this paper, we have proposed two new feature selection methods based on a tree growth algorithm (TGA) for EMG signals classification. In the first approach, two transfer functions are implemented to convert the continuous TGA into a binary version. For the second approach, the swap, crossover, and mutation operators are introduced in a modified binary tree growth algorithm for enhancing the exploitation and exploration behaviors. In this study, short time Fourier transform (STFT) is employed to transform the EMG signals into time-frequency representation. The features are then extracted from the STFT coefficient and form a feature vector. Afterward, the proposed feature selection methods are applied to evaluate the best feature subset from a large available feature set. The experimental results show the superiority of MBTGA not only in terms of feature reduction, but also the classification performance.
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Keywords:
feature selection; tree growth algorithm; electromyography; classification; time frequency features
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MDPI and ACS Style
Too, J.; Abdullah, A.R.; Mohd Saad, N.; Mohd Ali, N. Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals. Machines 2018, 6, 65. https://doi.org/10.3390/machines6040065
AMA Style
Too J, Abdullah AR, Mohd Saad N, Mohd Ali N. Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals. Machines. 2018; 6(4):65. https://doi.org/10.3390/machines6040065
Chicago/Turabian StyleToo, Jingwei, Abdul R. Abdullah, Norhashimah Mohd Saad, and Nursabillilah Mohd Ali. 2018. "Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals" Machines 6, no. 4: 65. https://doi.org/10.3390/machines6040065
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