Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury

被引:39
作者
Jordanic, Mislav [1 ,2 ]
Rojas-Martinez, Mnica [1 ,2 ]
Angel Mananas, Miguel [1 ,2 ]
Francesc Alonso, Joan [1 ,2 ]
机构
[1] Tech Univ Catalonia UPC, Biomed Engn Res Ctr CREB, Dept Automat Control ESAII, Barcelona, Spain
[2] Biomed Res Networking Ctr Bioengn & Biomat & Nano, Barcelona, Spain
来源
JOURNAL OF NEUROENGINEERING AND REHABILITATION | 2016年 / 13卷
关键词
Myoelectric control; Pattern recognition; High density electromyography; Incomplete spinal cord injury; MYOELECTRIC PATTERN-RECOGNITION; BRAIN-COMPUTER INTERFACES; SURFACE EMG; RESTORATION; FEATURES; STROKE; MUSCLE;
D O I
10.1186/s12984-016-0151-8
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Background: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention. Method: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. Results: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard). Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05). Conclusion: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences.
引用
收藏
页数:11
相关论文
共 46 条
[1]  
[Anonymous], 2013, P ISSNIP BIOSIGNALS, DOI [DOI 10.1109/BRC.2013.6487548, 10.1109/BRC.2013.6487548]
[2]  
[Anonymous], 1999, EUROPEAN RECOMMENDAT
[3]  
Baikun Wan, 2010, 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO), P1229, DOI 10.1109/ROBIO.2010.5723504
[4]   Exoskeleton Technology in Rehabilitation: Towards an EMG-Based Orthosis System for Upper Limb Neuromotor Rehabilitation [J].
Benitez, Luis Manuel Vaca ;
Tabie, Marc ;
Will, Niels ;
Schmidt, Steffen ;
Jordan, Mathias ;
Kirchner, Elsa Andrea .
JOURNAL OF ROBOTICS, 2013, 2013
[5]  
DE LUCA CJ, 1984, CRIT REV BIOMED ENG, V11, P251
[6]   The use of surface electromyography in biomechanics [J].
De Luca, CJ .
JOURNAL OF APPLIED BIOMECHANICS, 1997, 13 (02) :135-163
[7]   Customized interactive robotic treatment for stroke: EMG-triggered therapy [J].
Dipietro, L ;
Ferraro, M ;
Palazzolo, JJ ;
Krebs, HI ;
Volpe, BT ;
Hogan, N .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2005, 13 (03) :325-334
[8]   Classification of the myoelectric signal using time-frequency based representations [J].
Engelhart, K ;
Hudgins, B ;
Parker, PA ;
Stevenson, M .
MEDICAL ENGINEERING & PHYSICS, 1999, 21 (6-7) :431-438
[9]   Evaluation of intra-muscular EMG signal decomposition algorithms [J].
Farina, D ;
Colombo, R ;
Merletti, R ;
Olsen, HB .
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2001, 11 (03) :175-187
[10]   The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges [J].
Farina, Dario ;
Jiang, Ning ;
Rehbaum, Hubertus ;
Holobar, Ales ;
Graimann, Bernhard ;
Dietl, Hans ;
Aszmann, Oskar C. .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2014, 22 (04) :797-809