Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals
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 r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/6/4/65 |
| _version_ | 1819170699514740736 |
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| author | Jingwei Too Abdul Rahim Abdullah Norhashimah Mohd Saad Nursabillilah Mohd Ali |
| author_facet | Jingwei Too Abdul Rahim Abdullah Norhashimah Mohd Saad Nursabillilah Mohd Ali |
| author_sort | Jingwei Too |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T19:39:33Z |
| format | Article |
| id | doaj.art-f97fb17ffbbd4f41bcc2287ac6fe20d9 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-12-22T19:39:33Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-f97fb17ffbbd4f41bcc2287ac6fe20d92022-12-21T18:14:53ZengMDPI AGMachines2075-17022018-12-01646510.3390/machines6040065machines6040065Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric SignalsJingwei Too0Abdul Rahim Abdullah1Norhashimah Mohd Saad2Nursabillilah Mohd Ali3Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, MalaysiaFakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, MalaysiaFakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, MalaysiaFakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, MalaysiaElectromyography (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.https://www.mdpi.com/2075-1702/6/4/65feature selectiontree growth algorithmelectromyographyclassificationtime frequency features |
| spellingShingle | Jingwei Too Abdul Rahim Abdullah Norhashimah Mohd Saad Nursabillilah Mohd Ali Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals Machines feature selection tree growth algorithm electromyography classification time frequency features |
| title | Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals |
| title_full | Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals |
| title_fullStr | Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals |
| title_full_unstemmed | Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals |
| title_short | Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals |
| title_sort | feature selection based on binary tree growth algorithm for the classification of myoelectric signals |
| topic | feature selection tree growth algorithm electromyography classification time frequency features |
| url | https://www.mdpi.com/2075-1702/6/4/65 |
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