# EMG Pattern Classification by Split and Merge Deep Belief Network

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Pattern Recognition Process

#### 2.2. Feature Extraction

#### 2.3. Split and Merge Deep Belief Network

#### 2.4. Participants and EMG Signal Acquisition

#### 2.5. Training and Test

## 3. Results

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

BP | Back Propagation |

DAMV | Different Absolute Mean Value |

DBN | Deep Belief Network |

ECU | Extensor Carpi Ulnaris |

EMG | Electromyography |

FCU | Flexor Carpi Ulnaris |

GA | Genetic Algorithm |

GMM | Gaussian Mixture Model |

LDA | Linear Discriminant Analysis |

MAV | Mean Absolute Value |

MLP | Multi-Layer Perceptron |

RBM | Restricted Boltzmann Machine |

SM-DBN | Split and Merge Deep Belief Network |

SVM | Support Vector Machine |

ZC | Zero Crossing |

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**Figure 2.**We acquired two channel EMG signals. Channel 1 signals were measured from the flexor carpi ulnaris muscle, and channel 2 signals were measured from the extensor carpi ulnaris.

Parameter | Value |
---|---|

Channels | Flexor carpi ulnaris |

Extensor carpi ulnaris | |

Sampling rate | 1 kHz |

Filter | 10–500 Hz 2nd order Butterworth |

Data window | 166 ms Hamming window, 50% overlapped |

Features | DAMV, DASDV, MAV, ZC |

Number of Trials | DBN | SM-DBN | |
---|---|---|---|

1 | 13.09 | 10.82 | |

2 | 12.64 | 11.36 | |

3 | 9.45 | 8.82 | |

4 | 12.64 | 12.36 | |

5 | 11.18 | 9.55 | |

6 | 12.45 | 11.09 | |

7 | 11.64 | 10.73 | |

8 | 11.73 | 9.73 | |

9 | 12.18 | 11.27 | |

10 | 13.00 | 11.36 | |

Average | 12.00 | 10.709 | |

Standard-deviation | 1.086 | 1.050 | |

t-test | Confidence interval | 95% | |

[0.2873556, 2.2946444] | |||

t | 2.7027 | ||

df | 17.979 | ||

p-value | 0.0146 | ||

alternative hypothesis | True difference in means is not equal to 0. |

Number of Trials | SM-DBN | DBN | ||||
---|---|---|---|---|---|---|

Accuracy (%) | Sensitivity (TPR, %) | Specificity (FPR, %) | Accuracy (%) | Sensitivity (TPR, %) | Specificity (FPR, %) | |

1 | 89.18 | 89.27% | 2.90% | 86.91% | 86.98% | 3.56% |

2 | 88.64 | 88.73% | 3.08% | 87.36% | 87.44% | 3.46% |

3 | 91.18 | 91.29% | 2.34% | 90.55% | 90.83% | 2.53% |

4 | 87.64 | 87.74% | 3.37% | 87.36% | 87.43% | 3.46% |

5 | 90.45 | 90.58% | 2.55% | 88.82% | 89.02% | 3.02% |

6 | 88.91 | 89.11% | 3.00% | 87.55% | 87.80% | 3.42% |

7 | 89.27 | 89.34% | 2.90% | 88.36% | 88.36% | 3.16% |

8 | 90.27 | 90.31% | 2.59% | 88.27% | 88.37% | 3.19% |

9 | 88.73 | 88.87% | 3.04% | 87.82% | 87.74% | 3.28% |

10 | 88.64 | 88.70% | 3.07% | 87.00% | 87.12% | 3.48% |

Average | 89.29 | 89.39% | 2.88% | 88.00% | 88.11% | 3.26% |

Standard Deviation | 0.010522 | 0.010468119 | 0.003051907 | 0.01085 | 0.011446 | 0.003072 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Shim, H.-m.; An, H.; Lee, S.; Lee, E.H.; Min, H.-k.; Lee, S.
EMG Pattern Classification by Split and Merge Deep Belief Network. *Symmetry* **2016**, *8*, 148.
https://doi.org/10.3390/sym8120148

**AMA Style**

Shim H-m, An H, Lee S, Lee EH, Min H-k, Lee S.
EMG Pattern Classification by Split and Merge Deep Belief Network. *Symmetry*. 2016; 8(12):148.
https://doi.org/10.3390/sym8120148

**Chicago/Turabian Style**

Shim, Hyeon-min, Hongsub An, Sanghyuk Lee, Eung Hyuk Lee, Hong-ki Min, and Sangmin Lee.
2016. "EMG Pattern Classification by Split and Merge Deep Belief Network" *Symmetry* 8, no. 12: 148.
https://doi.org/10.3390/sym8120148