**Author details**

22 Computational Intelligence in Electromyography Analysis: A Perspective on Current Applications and Future Challenges

pattern of motor unit recruitment. Thus, although the spastic or hemiplegic patient lacks of proper voluntary control, it is expected that the system will be able to capture small voluntary changes in motor recruitment, as a result of his desire to do so. These changes can then be used to guide the movements of the virtual member. In so doing, the virtual feedback operates as a guide, indicating that the current mental strategy (neuromotor control) is correct and should

The widespread use of automatic tools for decomposing EMG signals is dependent on how easy is to detect MUAPs from the surface of the skin, on the identification of new applications, either online or offline, which might employ the results of the EMG decomposition, and also

Ideally the sensors used for detecting MUAPs from the surface of the skin should be easy of applying and using. As a number of applications require the use of multiple sensors organized on an array the solution to this issue becomes more complex. Therefore, further research on this area, with the aim of developing sensors that allow for the reproduction of experiments

Most applications found in the area of EMG decomposition are solely focused on the development of the automatic tool for decomposing the EMG signal, or in the classification and discrimination of MUAPs. Therefore, the identification of new useful applications are required in order to disseminate the relevance of the technique to other areas. For instance, the use of motor unit information could be employed in robotics, myofeedback, and

A major limitation of the results in the area of EMG decomposition is that the developed tools are not shared by researchers. The sharing of these tools together with EMG signal databases could be beneficial to the widespread use of the tools. Furthermore, this would

Considering the application of artefact removal from biomedical signals, the development of algorithms for reducing the influence of noise over signals in real time, without a priori knowledge about the origins and characteristics of the noise, are required. In this way the application of adaptive techniques such as Empirical Mode Decomposition should be further

It is also expected that the use of these filters can be part of the solution of the problem of EMG decomposition by directly decomposing the raw EMG signal into its motor unit action potential trains. If such filters are developed, EMG decomposition tools could be embedded

The authors would like to express their gratitude to the Research Foundation of the State of Minas Gerais (FAPEMIG), The National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES).

be encouraged, reinforcing the process of neural reorganization (neuroplasticity).

on the sharing and availability of developed tools to clinicians and researchers.

**6. Future directions**

Computational Intelligence in Electromyography Analysis –

human-machine interface development.

allow researchers to objectively compare distinct solutions.

in hardware speeding up the results from multichannel data.

is required.

282

exploited.

**Acknowledgements**

Adriano O. Andrade and Alcimar B. Soares

*Faculty of Electrical Engineering, Laboratory of Biomedical Engineering, Federal University of Uberlândia, Uberlândia, Brazil*

Slawomir J. Nasuto

*School of Systems Engineering, University of Reading, Reading, United Kingdom*

Peter J. Kyberd

*Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada*

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© 2012 Sakakibara et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Sakakibara et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Sphincter EMG for Diagnosing Multiple System** 

Ryuji Sakakibara, Tomoyuki Uchiyama, Tatsuya Yamamoto, Fuyuki Tateno,

One of the hallmarks in the pathology of multiple system atrophy (MSA) is neuronal loss in the sacral Onuf's nucleus11,33,37. Onuf's nucleus plays a key role in urinary and fecal continence12. Neurons in this nucleus receive not only cortical inputs, but also noradrenergic and serotonergic facilitatory inputs via interneurons from various brainstem structures, including the pontine urine-storage center57,68. External anal sphincter (EAS) electromyography (EMG) is an established method to detect neurogenic change in motor unit potentials (MUP), which mostly reflects denervation and reinnervation of the sphincter muscle30. The significance of the EAS-EMG in MSA has been well known30,69,74. Physiologically, external urethral sphincter (EUS) and EAS share sacral pudendal innervation from Onuf's nucleus20. In this article, we review the normal physiology and pathophysiology of the lower urinary tract and the lower gastrointestinal tract briefly, the current methods and interpretations of EAS or EUS-EMG, and sphincter EMG in autonomic

**2. Physiology and pathophysiology of the lower urinary tract** 

The lower urinary tract consists of two major components, the bladder and urethra. The bladder is abundant with muscarinic M2,3 receptors (contraction) and adrenergic beta 3 receptors (relaxation)12. The urethra is abundant with adrenergic alpha 1A/D receptors (contraction) and nicotinic receptors (contraction) **(Fig. 1)**. The storage and emptying functions need an intact neuraxis that involves almost all parts of the nervous system48. This is in contrast to postural hypotension, which arises due to lesions below the medullary

**Atrophy and Related Disorders** 

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/45880

**1. Introduction** 

disorders.

circulation center56.

Tomonori Yamanishi, Masahiko Kishi and Yohei Tsuyusaki
