**Author details**

Rissanen Saara M., Tarvainen Mika P. and Karjalainen Pasi A. *Department of Applied Physics, University of Eastern Finland, Kuopio, Finland*

Kankaanpää Markku *Department of Physical and Rehabilitation Medicine, Tampere University Hospital, Tampere, Finland*

#### **6. References**

22 Will-be-set-by-IN-TECH

about 25 % of cases [48]. On the basis of our discrimination results, further research and clinical studies are suggested for evaluating the sensitivity of the developed approaches in patients with different types of PD and in patients with early stages of PD. In addition, the ability of EMG and acceleration signal features in discriminating between PD patients and

In this chapter, we presented two approaches for quantifying the effects of PD treatment (medication and DBS) on the basis of the extracted EMG and acceleration signal features. The presented approaches were developed in our studies [30, 31]. By using the developed approaches, we could detect DBS- and medication-induced improvements in the neuromuscular and motor function of PD patients. This result is encouraging because the widely used method for evaluating the efficacy of PD treatment is subjective. However, the sensitivity of the developed approaches should be quantified with a larger number of PD

The need for finding objective methods for PD diagnosis and for quantifying the disease progression and the efficacy of treatment is well known [2, 11, 24]. We hope that our results [28–32] can help in creating a practical method for quantifying motor impairment in PD and the effects of treatment on individual PD patients. However, in order to be more sensitive than the traditional methods, it is probable that a combination of several objective methods

There is currently a lot of effort for determining objective methods and characteristics for PD [2, 11, 24]. One important goal of current research is to determine criteria for the pre-motor and pre-clinical phases of PD [39]. In surface EMG studies, the sensitivity of surface EMG signal features in detecting PD patients before the actual diagnosis of PD should be studied. It will be important to analyze differences in the signal characteristics between PD patients and other patients with similar symptoms. These other similar diseases form currently a significant reason for the wrong diagnosis of PD [17]. It has been observed that surface EMG and kinematic measurements can provide information about the effects of PD treatments (medication and DBS). The ability of these measurements in helping the optimal adjustment

This work was supported by the Academy of Finland under Project 252748.

*Department of Applied Physics, University of Eastern Finland, Kuopio, Finland*

*Department of Physical and Rehabilitation Medicine, Tampere University Hospital, Tampere, Finland*

Rissanen Saara M., Tarvainen Mika P. and Karjalainen Pasi A.

other patients with similar symptoms should be studied.

Computational Intelligence in Electromyography Analysis –

patients.

242

will be needed for PD.

**5.5. Future directions**

**Acknowledgments**

**Author details**

Kankaanpää Markku

of these treatments should be evaluated.


Computational Intelligence in Electromyography Analysis –

	- [16] Grassberger, P. & Procaccia, I. [1983]. Characterization of strange attractors, *Phys. Rev. Lett.* 50(5): 346–349.

[33] Robichaud, J., Pfann, K., Comella, C., Brandabur, M. & Corcos, D. [2004]. Greater impairment of extension movements as compared to flexion movements in Parkinson's

245

Feature Extraction Methods for Studying Surface

Electromyography and Kinematic Measurements in Parkinson's Disease

[34] Robichaud, J., Pfann, K., Comella, C. & Corcos, D. [2002]. Effect of medication on EMG patterns in individuals with Parkinson's disease, *Mov. Disord.* 17(5): 950–960. [35] Robichaud, J., Pfann, K., Leurgans, S., Vaillancourt, D., Comella, C. & Corcos, D. [2009]. Variability of EMG patterns: a potential neurophysiological marker of Parkinson's

[36] Roiz, R., Cacho, E., Pazinatto, M., Reis, J., Cliquet, A. & Barasnevicius-Quagliato, E. [2010]. Gait analysis comparing Parkinson's disease with healthy elderly subjects., *Arq.*

[37] Salenius, S., Avikainen, S., Kaakkola, S., Hari, R. & Brown, P. [2002]. Defective cortical drive to muscle in Parkinson's disease and its improvement with levodopa, *Brain*

[38] Savica, R., Rocca, W. & Ahlskog, J. [2010]. When does Parkinson disease start?, *Arch.*

[39] Stern, M., Lang, A. & Poewe, W. [2012]. Toward a redefinition of Parkinson's disease?,

[40] Strambi, S., Rossi, B., de Michele, G. & Sello, S. [2004]. Effect of medication in Parkinson's

[41] Sturman, M., Vaillancourt, D., Metman, L., Bakay, R. & Corcos, D. [2004]. Effects of subthalamic nucleus stimulation and medication on resting and postural tremor in

[42] Sturman, M., Vaillancourt, D., Metman, L., Sierens, D., Bakay, R. & Corcos, D. [2007]. Deep brain stimulation and medication for parkinsonian tremor during secondary tasks,

[43] Svehlík, M., Zwick, E., Steinwender, G., Linhart, W., Schwingenschuh, P., Katschnig, P., Ott, E. & Enzinger, C. [2009]. Gait analysis in patients with Parkinson's disease off

[44] Tabbal, S., Ushe, M., Mink, J., Revilla, F., Wernle, A., Hong, M., Karimi, M. & Perlmutter, J. [2008]. Unilateral subthalamic nucleus stimulation has a measurable ipsilateral effect

[46] Tarvainen, M., Ranta-aho, P. & Karjalainen, P. [2002]. An advanced detrending method

[47] Theodoridis, S. & Koutroumbas, K. [2006]. *Pattern recognition*, Elsevier/Academic Press,

[48] Tolosa, E., Wenning, G. & Poewe, W. [2006]. The diagnosis of Parkinson's disease, *Lancet*

[49] Tucha, O., Mecklinger, L., Thome, J., Reiter, A., Alders, G., Sartor, H., Naumann, M. & Lange, K. [2006]. Kinematic analysis of dopaminergic effects on skilled handwriting

with application to HRV analysis, *IEEE Trans. Biomed. Eng.* 49(2): 172–175.

movements in Parkinson's disease, *J. Neural. Transm.* 113(5): 609–623.

on rigidity and bradykinesia in Parkinson disease, *Exp. Neurol.* 211(1): 234–242. [45] Takens, F. [1981]. Detecting strange attractors in turbulence, *in* D. Rand & L.-S. Young (eds), *Dynamical Systems and Turbulence, Warwick 1980*, Vol. 898 of *Lecture Notes in*

disease: a wavelet analysis of EMG signals, *Med. Eng. Phys.* 26(4): 279–290.

dopaminergic therapy, *Arch. Phys. Med. Rehabil.* 90(11): 1880–1886.

*Mathematics*, Springer Berlin / Heidelberg, pp. 366–381.

disease, *Exp. Brain Res.* 156(2): 240–254.

disease, *Clin. Neurophysiol.* 120(2): 390–397.

Parkinson's disease, *Brain* 127(9): 2131–2143.

*Neuropsiquiatr.* 68(1): 81–86.

125(3): 491–500.

*Neurol.* 67(7): 798–801.

*Mov. Disord.* 27(1): 54–60.

*Mov. Disord.* 22(8): 1157–1163.

USA.

*Neurol.* 5(1): 75–86.


[33] Robichaud, J., Pfann, K., Comella, C., Brandabur, M. & Corcos, D. [2004]. Greater impairment of extension movements as compared to flexion movements in Parkinson's disease, *Exp. Brain Res.* 156(2): 240–254.

24 Will-be-set-by-IN-TECH

[16] Grassberger, P. & Procaccia, I. [1983]. Characterization of strange attractors, *Phys. Rev.*

[17] Grosset, D., Grosset, K., Okun, M. & Fernandez, H. [2009]. *Parkinson's disease - Clinician's*

[18] Jankovic, J. [2008]. Parkinson's disease: clinical features and diagnosis, *J. Neurol.*

[20] Karlsson, S., Yu, J. & Akay, M. [2000]. Time-frequency analysis of myoelectric signals during dynamic contractions: A comparative study, *IEEE Trans. Biomed. Eng.*

[21] Levin, J., Krafczyk, S., Valkoviˇc, P., Eggert, T., Claassen, J. & Bötzel, K. [2009]. Objective measurement of muscle rigidity in parkinsonian patients treated with subthalamic

[22] Marek, K., Jennings, D., Tamagnan, G. & Seibyl, J. [2008]. Biomarkers for Parkinson's disease: tools to assess Parkinson's disease onset and progression, *Ann. Neurol.* 64 (suppl.

[23] Meigal, A., Rissanen, S., Tarvainen, M., Karjalainen, P., Iudina-Vassel, I., Airaksinen, O. & Kankaanpää, M. [2009]. Novel parameters of surface EMG in patients with Parkinson's disease and healthy young and old controls, *J. Electromyogr. Kinesiol.* 19(3): e206–e213. [24] Morgan, J., Mehta, S. & Sethi, K. [2010]. Biomarkers in Parkinson's disease, *Curr. Neurol.*

[25] Park, H., Kim, J., Paek, S., Jeon, B., Lee, J. & Chung, C. [2009]. Cortico-muscular coherence increases with tremor improvement after deep brain stimulation in Parkinson's disease,

[26] Pfann, K., Buchman, A., Comella, C. & Corcos, D. [2001]. Control of movement distance

[27] Richman, J. & Moorman, J. [2000]. Physiological time-series analysis using approximate entropy and sample entropy, *Am. J. Physiol. Heart Circ. Physiol.* 278(6): H2039–H2049. [28] Rissanen, S., Kankaanpää, M., Meigal, A., Tarvainen, M., Nuutinen, J., Tarkka, I., Airaksinen, O. & Karjalainen, P. [2008]. Surface EMG and acceleration signals in Parkinson's disease: feature extraction and cluster analysis, *Med. Biol. Eng. Comput.*

[29] Rissanen, S., Kankaanpää, M., Tarvainen, M., Meigal, A., Nuutinen, J., Tarkka, I., Airaksinen, O. & Karjalainen, P. [2009]. Analysis of dynamic voluntary muscle

[31] Rissanen, S., Kankaanpää, M., Tarvainen, M., Nuutinen, J., Airaksinen, O. & Karjalainen, P. [2011]. EMG and acceleration signal analysis for quantifying the effects of medication

[32] Rissanen, S., Kankaanpää, M., Tarvainen, M., Nuutinen, J., Tarkka, I., Airaksinen, O. & Karjalainen, P. [2007]. Analysis of surface EMG signal morphology in Parkinson's

in Parkinson's disease, *Conf. Proc. IEEE Eng. Med. Biol. Soc.*, pp. 7496–7499.

contractions in Parkinson's disease, *IEEE Trans. Biomed. Eng.* 56(9): 2280–2288. [30] Rissanen, S., Kankaanpää, M., Tarvainen, M., Novak, V., Novak, P., Hu, K., Manor, B., Airaksinen, O. & Karjalainen, P. [2011]. Analysis of EMG and acceleration signals for quantifying the effects of deep brain stimulation in Parkinson's disease, *IEEE Trans.*

[19] Jolliffe, I. [2002]. *Principal Component Analysis*, Springer-Verlag, New York.

*Lett.* 50(5): 346–349.

244

47(2): 228–238.

2): S111–S121.

46(9): 849–858.

*desk reference*, Manson Publishing Ltd., London.

*Neurosurg. Psychiatry* 79(4): 368–376.

Computational Intelligence in Electromyography Analysis –

stimulation, *Mov. Disord.* 24(1): 57–63.

*Neurosci. Rep.* 10(6): 423–430.

*Neuroreport* 20(16): 1444–1449.

*Biomed. Eng.* 58(9): 2545–2553.

disease, *Physiol. Meas.* 28(12): 1507–1521.

in Parkinson's disease, *Mov. Disord.* 16(6): 1048–1065.


Computational Intelligence in Electromyography Analysis –

	- [50] Vaillancourt, D. & Newell, K. [2000]. The dynamics of resting and postural tremor in Parkinson's disease, *Clin. Neurophysiol.* 111(11): 2046–2056.
	- [51] Vaillancourt, D., Prodoehl, J., Metman, L., Bakay, R. & Corcos, D. [2004]. Effects of deep brain stimulation and medication on bradykinesia and muscle activation in Parkinson's disease, *Brain* 127(3): 491–504.
	- [52] Vaillancourt, D., Prodoehl, J., Sturman, M., Bakay, R., Metman, L. & Corcos, D. [2006]. Effects of deep brain stimulation and medication on strength, bradykinesia, and electromyographic patterns of the ankle joint in Parkinson's disease, *Mov. Disord.* 21(1): 50–58.
	- [53] Webber, C. & Zbilut, J. [1994]. Dynamical assessment of physiological systems and states using recurrence plot strategies, *J. Appl. Physiol.* 76(2): 965–973.
	- [54] Welch, P. [1967]. The used of FFT for estimation of power spectra:a method based on time averaging over short modified periodograms, *IEEE Trans. Audio Electroacoust.* 15: 70–73.
	- [55] Xia, R. & Rymer, W. [2004]. The role of shortening reaction in mediating rigidity in Parkinson's disease, *Exp. Brain Res.* 156(4): 524–528.

© 2012 Ishii, 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 Ishii, 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.

**Distinction of Abnormality of Surgical Operation** 

Recently, minimally invasive surgery such as endoscopic surgery is taking the place of laparotomy. In the field of minimally invasive surgery, a typical commercial surgical robot, such as the da Vinci system produced by Intuitive Surgical Inc., is currently in clinical use. In the robot supported surgery, master-slave system is employed. In such master-slave systems, usually motions of the master device are detected by sensors, and the slave device is controlled to follow the behavior of the master device based on the measured information

To perform a robotic surgery, a surgeon must have considerable skill. Operation by an unskilled surgeon may result in serious malpractice. Therefore, development of a system which urges an appropriate operation to the unskilled surgeon is in demand. As described in (Tanoue et al., 2007), for training of the robotic surgery, training box or simulator has

Recently, in order to help surgeon's dexterity, force feedback to a surgeon through the master device of a surgical robot has been studied in (Ishii et al., 2011). In order to perform safe surgery, (Ikuta et al., 2007) proposed safe operation strategies, called "Safety operation space" and "Variable compliance system" for the surgical robot. The former can prevent collision between the forceps and organs. The latter can reduce the collision force between

In addition, training systems to practice operation of surgical robot through simulation using virtual reality environment (e.g. Tokuda et al., 2009), and navigation systems which guide a surgical instrument to the targeted location during the robotic surgery (e.g. Krupa et

by those sensors. Therefore, even the mistaken operation will be reflected.

**on the Basis of Surface EMG Signals** 

Additional information is available at the end of the chapter

Chiharu Ishii

http://dx.doi.org/10.5772/50601

**1. Introduction** 

been generally used.

the forceps and organs.

al., 2003), have been studied.
