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Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis

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Abstract

We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson’s disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.

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References

  1. Clancy EA, Farina D, Filligoi G (2004) Single-channel techniques for information extraction from the surface EMG signal. Electromyography: physiology, engineering, and noninvasive applications. Wiley-IEEE Press, USA

    Google Scholar 

  2. De Lau LML, Breteler MMB (2006) Epidemiology of Parkinson’s disease. Lancet Neurol 5:525–535

    Article  Google Scholar 

  3. De Michele G, Sello S, Carboncini MC, Rossi B, Strambi S (2003) Cross-correlation time–frequency analysis for multiple EMG signals in Parkinson’s disease: wavelet approach. Med Eng Phys 25:361–369

    Article  Google Scholar 

  4. Eerola J, Tienari PJ, Kaakkola S, Nikkinen P and Launes J (2005) How useful is [123I]β-CIT SPECT in clinical practise? J Neurol Neurosurg Psychiatry 76:1211–1216

    Article  Google Scholar 

  5. Fattorini L, Felici F, Filligoi GC, Traballesi M, Farina D (2005) Influence of high motor unit synchronization levels on non-linear and spectral variables of the surface EMG. J Neurosci Methods 143:133–139

    Article  Google Scholar 

  6. Flament D, Vaillancourt DE, Kempf T, Shannon K, Corcos DM (2003) EMG remains fractioned in Parkinson’s disease, despite practice-related improvements in performance. Clin Neurophysiol 114:2385–2396

    Article  Google Scholar 

  7. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, USA

    MATH  Google Scholar 

  8. Kuusela TA, Jartti TT, Tahvanainen KUO, Kaila TJ (2002) Nonlinear methods of biosignal analysis in assessing terbutaline-induced heart rate and blood pressure changes. Am J Physiol Heart Circ Physiol 282:H773–H781

    Google Scholar 

  9. Pfann KD, Buchman AS, Comella CL, Corcos DM (2001) Control of movement distance in Parkinson’s disease. Mov Disord 16(6):1048–1065

    Article  Google Scholar 

  10. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278:H2039–H2049

    Google Scholar 

  11. Rissanen S, Kankaanpää M, Tarvainen MP, Nuutinen J, Tarkka IM, Airaksinen O, Karjalainen PA (2007) Analysis of surface EMG signal morphology in Parkinson’s disease. Physiol Meas 28:1507–1521

    Article  Google Scholar 

  12. Robichaud JA, Pfann KD, Comella CL, Corcos DM (2002) Effect of medication on EMG patterns in individuals with Parkinson’s disease. Mov Disord 17(5):950–960

    Article  Google Scholar 

  13. Robichaud JA, Pfann KD, Comella CL, Brandabur M, Corcos DM (2004) Greater impairment of extension movements as compared to flexion movements in Parkinson’s disease. Exp Brain Res 156:240–254

    Article  Google Scholar 

  14. Robichaud JA, Pfann KD, Vaillancourt DE, Comella CL, Corcos DM (2005) Force control and disease severity in Parkinson’s disease. Mov Disord 20(4):441–450

    Article  Google Scholar 

  15. Sturman MM, Vaillancourt DE, Metman LV, Bakay RAE, Corcos DM (2004) Effects of subthalamic nucleus stimulation and medication on resting and postural tremor in Parkinson’s disease. Brain 127:2131–2143

    Article  Google Scholar 

  16. Tarvainen MP, Ranta-aho PO, Karjalainen PA (2002) An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng 49(2):172–175

    Article  Google Scholar 

  17. Tolosa E, Wenning G, Poewe W (2006) The diagnosis of Parkinson’s disease. Lancet Neurol 5:75–86

    Article  Google Scholar 

  18. Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, USA

    MATH  Google Scholar 

  19. Vaillancourt DE, Newell KM (2000) The dynamics of resting and postural tremor in Parkinson’s disease. Clin Neurophysiol 111:2046–2056

    Article  Google Scholar 

  20. Valls-Solé J, Valldeoriola F (2002) Neurophysiological correlate of clinical signs in Parkinson’s disease. Clin Neurophysiol 113:792–805

    Article  Google Scholar 

  21. Webber CL, Zbilut JP (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76(2):965–973

    Google Scholar 

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Authors and Affiliations

  1. Department of Physics, University of Kuopio, P.O.Box 1627, 70211, Kuopio, Finland

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

  2. Department of Physical and Rehabilitation Medicine, Kuopio University Hospital, Kuopio, Finland

    Saara M. Rissanen, Markku Kankaanpää & Olavi Airaksinen

  3. Petrozavodsk State University, Petrozavodsk, Russia

    Alexander Meigal

  4. Department of Neurology, Kuopio University Hospital, Kuopio, Finland

    Juho Nuutinen

  5. Brain Research and Rehabilitation Center Neuron, Kuopio, Finland

    Ina M. Tarkka

Authors
  1. Saara M. Rissanen
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  2. Markku Kankaanpää
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  3. Alexander Meigal
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  4. Mika P. Tarvainen
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  5. Juho Nuutinen
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  6. Ina M. Tarkka
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  7. Olavi Airaksinen
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  8. Pasi A. Karjalainen
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Corresponding author

Correspondence to Saara M. Rissanen.

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Rissanen, S.M., Kankaanpää, M., Meigal, A. et al. Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis. Med Biol Eng Comput 46, 849–858 (2008). https://doi.org/10.1007/s11517-008-0369-0

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  • DOI: https://doi.org/10.1007/s11517-008-0369-0

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