Skip to main content

Advertisement

Springer Nature Link
Log in

A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients’ impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Allin S, Baker N, Eckel E, Ramanan D (2010) Robust tracking of the upper limb for functional stroke assessment. IEEE Trans Neural Syst Rehabil Eng 18(5):542–550. doi:10.1109/TNSRE.2010.2047267

    Article  PubMed  Google Scholar 

  2. American Association for Artificial Intelligence: an empirical study of the naive Bayes classifier (2001)

  3. Asghari Oskoei M, Hu H (2007) Myoelectric control systems—a survey. Biomed Signal Process Control 2(4):275–294

    Article  Google Scholar 

  4. Bonato P, Roy S, Knaflitz M, De Luca C (2001) Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng 48(7):745–753

    Article  CAS  PubMed  Google Scholar 

  5. Brunnstrom S (1966) Motor testing procedures in hemiplegia: based on sequential recovery stages. Phys Ther 46(4):357–375

    CAS  PubMed  Google Scholar 

  6. Brunnström S (1970) Movement therapy in hemiplegia: a neurophysiological approach. Medical Dept., Harper & Row, New York

    Google Scholar 

  7. Carey L (2012) Stroke rehabilitation: insights from neuroscience and imaging. OUP, Oxford

    Book  Google Scholar 

  8. Chen Y, Zhou Y, Cheng X, Mi Y (2013) Upper limb motion recognition based on two-step SVM classification method of surface EMG. Int J Control Autom 6(3):249–266

    Google Scholar 

  9. Chiang J, Wang J, McKeown M (2008) A hidden Markov, multivariate autoregressive (HMM-mAR) network framework for analysis of surface EMG (sEMG) data. IEEE Trans Signal Process 56(8):4069–4081

    Article  Google Scholar 

  10. Chiang J, Wang Z, McKeown M (2008) A windowed eigenspectrum method for multivariate semg classification during reaching movements. IEEE Signal Process Lett 15:293–296. doi:10.1109/LSP.2008.917801

    Article  Google Scholar 

  11. Chiang J, Wang Z, McKeown M (2006) A time-varying eigenspectrum/SVM method for sEMG classification of reaching movements in healthy and stroke subjects. In: 2006 IEEE international conference on acoustics, speech and signal processing, vol 2. ICASSP 2006 Proceedings, pp II–II. doi:10.1109/ICASSP.2006.1660561

  12. Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278

    Article  Google Scholar 

  13. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  14. De Luca C (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13(2):135–163

    Article  Google Scholar 

  15. Dewey H, Sherry L, Collier J (2007) Stroke rehabilitation 2007: what should it be? Int J Stroke 2(3):191–200

    Article  PubMed  Google Scholar 

  16. Farina D, Merletti R, Enoka R (2004) The extraction of neural strategies from the surface emg. J Appl Physiol 96(4):1486–1495

    Article  PubMed  Google Scholar 

  17. Feigin V, Forouzanfar M, Krishnamurthi R, Mensah G, Connor M, Bennett D, Moran A, Sacco R, Anderson L, Truelsen T, O’Donnell M, Venketasubramanian N, Barker-Collo S, Lawes C, Wang W, Shinohara Y, Witt E, Ezzati M, Naghavi M (2014) Global and regional burden of stroke during 1990–2010: findings from the global burden of disease study 2010. Lancet 383(9913):245–255

    Article  PubMed  PubMed Central  Google Scholar 

  18. Geng Y, Zhang L, Tang D, Zhang X, Li G (2013) Pattern recognition based forearm motion classification for patients with chronic hemiparesis. In: Proceedings of IEEE EMBS, pp 5918–5921

  19. Ghasemzadeh H, Jafari R, Prabhakaran B (2010) A body sensor network with electromyogram and inertial sensors: multimodal interpretation of muscular activities. IEEE Trans Inf Technol Biomed 14(2):198–206

    Article  PubMed  Google Scholar 

  20. Gladstone D, Danells C, Black S (2002) The fugl-meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair 16(3):232–240

    Article  PubMed  Google Scholar 

  21. Ho YC, Agrawala A (1968) On pattern classification algorithms introduction and survey. Proc IEEE 56(12):2101–2114

    Article  Google Scholar 

  22. Huang S, Luo C, Ye S, Liu F, Xie B, Wang C, Yang L, Huang Z, Wu J (2012) Motor impairment evaluation for upper limb in stroke patients on the basis of a microsensor. Int J Rehabil Res 35(2):161–169

    Article  PubMed  Google Scholar 

  23. Kaiser J (1993) Some useful properties of teager’s energy operators. In: 1993 IEEE international conference on acoustics, speech, and signal processing, vol 3. ICASSP-93, pp 149–152

  24. Kaiser J (1990) On a simple algorithm to calculate the ‘energy’ of a signal. In: 1990 international conference on acoustics, speech, and signal processing, vol 1. ICASSP-90, pp 381–384

  25. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 conference on emerging artificial intelligence applications in computer engineering: real word AI systems with applications in eHealth, HCI, information retrieval and pervasive technologies. IOS Press, Amsterdam, The Netherlands, pp 3–24

  26. Krzanowski WJ (ed) (1988) Principles of multivariate analysis: a user’s perspective. Oxford University Press, Inc., New York

    Google Scholar 

  27. Lee JD, Cheng, YT, Liu LC, Wu CY (2007) A study of evaluation parameters for stroke patients’ Brunnstrom recovery stages. In: IEEE Region 10 Annual International Conference, Proceedings/TENCON

  28. Liparulo L, Proietti A, Panella M (2015) Fuzzy clustering using the convex hull as geometrical model. Adv Fuzzy Syst 15:1–13. doi:10.1155/2015/265135

    Google Scholar 

  29. Liparulo L, Proietti A, Panella M (2013) Fuzzy membership functions based on point-to-polygon distance evaluation. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ-IEEE 2013), pp 1–8. doi:10.1109/FUZZ-IEEE.2013.6622449

  30. Liparulo L, Proietti A, Panella M (2015) Improved online fuzzy clustering based on unconstrained kernels. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ-IEEE 2015), pp 1–8. doi:10.1109/FUZZ-IEEE.2015.7338065

  31. Lyden P, Brott T, Tilley B, Welch K, Mascha E, Levine S, Haley E, Grotta J, Marler J (1994) Improved reliability of the nih stroke scale using video training. Stroke 25(11):2220–2226

    Article  CAS  PubMed  Google Scholar 

  32. Mahoney FI, Barthel DW (1965) Functional evaluation: the barthel index. Md State Med J 14:61–65

    CAS  PubMed  Google Scholar 

  33. Maisto M, Panella M, Liparulo L, Proietti A (2013) An accurate algorithm for the identification of fingertips using an RGB-D camera. IEEE J Emerg Sel Top Circuits Syst 3(2):272–283. doi:10.1109/JETCAS.2013.2256830

    Article  Google Scholar 

  34. Mamdani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13

    Article  Google Scholar 

  35. Mitra S, Pal S, Mitra P (2002) Data mining in soft computing framework: a survey. IEEE Trans Neural Netw 13(1):3–14

    Article  CAS  PubMed  Google Scholar 

  36. Murphy T, Corbett D (2009) Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci 10(12):861–872

    Article  CAS  PubMed  Google Scholar 

  37. Nadeau C, Bengio Y (2003) Inference for the generalization error. Mach Learn 52(3):239–281. doi:10.1023/A:1024068626366

    Article  Google Scholar 

  38. Naghdi S, Ansari NN, Mansouri K, Hasson S (2010) A neurophysiological and clinical study of Brunnstrom recovery stages in the upper limb following stroke. Brain Injury 24(11):1372–1378

    Article  PubMed  Google Scholar 

  39. National Stroke Foundation (2010) Clinical guidelines for stroke management 2010, p 79

  40. National Stroke Audit—Rehabilitation Services Report 2012. National Stroke Foundation, Melbourne (2012)

  41. O’Dwyer NJ, Ada L, Neilson PD (1996) Spasticity and muscle contracture following stroke. Brain 119(5):1737–1749. doi:10.1093/brain/119.5.1737

    Article  PubMed  Google Scholar 

  42. Pal N (1999) Soft computing for feature analysis. Fuzzy Sets Syst 103(2):201–221

    Article  Google Scholar 

  43. Panella M (2011) Advances in biological time series prediction by neural networks. Biomed Signal Process Control 6(2):112–120. doi:10.1016/j.bspc.2010.09.006

    Article  Google Scholar 

  44. Patel S, Hughes R, Hester T, Stein J, Akay M, Dy J, Bonato P (2010) A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc IEEE 98(3):450–461. doi:10.1109/JPROC.2009.2038727

    Article  Google Scholar 

  45. Patel S, Hughes R, Hester T, Stein J, Akay M, Dy J, Bonato P (2010) Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology. In: 2010 annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 6858–6861

  46. Pietrusinski M, Severini G, Cajigas I, Mavroidis C, Bonato P (2012) Design of a gait training device for control of pelvic obliquity. In: 2012 annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 3620–3623

  47. Proietti, A., Panella, M., Leccese, F., Svezia, E. (2015) Dust detection and analysis in museum environment based on pattern recognition. Measurement 66:62–72. doi:10.1016/j.measurement.2015.01.019

    Article  Google Scholar 

  48. Rizzi A, Buccino NM, Panella M, Uncini A (2008) Genre classification of compressed audio data. In: Proceedings of IEEE workshop on multimedia signal processing (MLSP 2008), pp 654–659 doi:10.1109/MMSP.2008.4665157

  49. Rizzi A, Panella M, Mascioli FMF, Martinelli G (2000) A recursive algorithm for fuzzy Min–Max networks. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks (IJCNN 2000), vol 6, pp 541–546

  50. Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of Relieff and RRelieff. Mach Learn 53(1–2):23–69. doi:10.1023/A:1025667309714

    Article  Google Scholar 

  51. Rokach L, Maimon O (2008) Data mining with decision trees: theroy and applications. World Scientific Publishing Co. Inc., River Edge

    Google Scholar 

  52. Safaz I, Yilmaz B, Yaar E, Alaca R (2009) Brunnstrom recovery stage and motricity index for the evaluation of upper extremity in stroke: analysis for correlation and responsiveness. Int J Rehabil Res 32(3):228–231

    Article  PubMed  Google Scholar 

  53. Schiemanck S, Kwakkel G, Post M, Kappelle L, Prevo A (2006) Predicting long-term independency in activities of daily living after middle cerebral artery stroke: Does information from mri have added predictive value compared with clinical information? Stroke 37(4):1050–1054

    Article  PubMed  Google Scholar 

  54. Sedgwick P (2012) Pearson’s correlation coefficient. BMJ. doi:10.1136/bmj.e4483

    PubMed  Google Scholar 

  55. Shah S, Harasymiw S, Stahl P (1986) Stroke rehabilitation: outcome based on Brunnstrom recovery stages. Occup Ther J Res 6(6):365–376

  56. Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Article  Google Scholar 

  57. Strong K, Mathers C, Bonita R (2007) Preventing stroke: saving lives around the world. Lancet Neurol 6(2):182–187

    Article  PubMed  Google Scholar 

  58. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Inc., New York

    Google Scholar 

  59. Teasell R, Meyer M, McClure A, Pan C, Murie-Fernandez M, Foley N, Salter K (2009) Stroke rehabilitation: an international perspective. Top Stroke Rehabil 16(1):44–56

    Article  PubMed  Google Scholar 

  60. Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Academic Press, Burlington

    Google Scholar 

  61. Urra O, Casals A, Jana R (2013) Evaluating spatial characteristics of upper-limb movements from EMG signals. In: XIII Mediterranean conference on medical and biological engineering and computing, vol 56(8), pp1795–1798

  62. Yu L, Wang J, Fang Q, Wang Y (2012) Brunnstrom stage automatic evaluation for stroke patients using extreme learning machine. In: IEEE BioCAS Conf, pp 380–383

  63. Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  64. Zadeh LA (1971) Similarity relations and fuzzy orderings. Inf Sci 3(2):177–200

    Article  Google Scholar 

  65. Zadeh LA (1996) Fuzzy sets and their application to pattern classification and clustering analysis. In: Klir GJ, Yuan B (eds) Fuzzy sets, fuzzy logic, and fuzzy systems. World Scientific Publishing Co. Inc., River Edge, pp 355–393

    Chapter  Google Scholar 

  66. Zhang Z, Fang Q, Gu X (2014) Fuzzy inference system based automatic Brunnstrom stage classification for upper-extremity rehabilitation. Expert Syst Appl 41(4, Part 2):1973–1980. doi:10.1016/j.eswa.2013.08.094

    Article  Google Scholar 

  67. Zhang X, Zhou P (2012) High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans Biomed Eng 59(6):1649–1657

    Article  PubMed  Google Scholar 

  68. Zhang Z, Fang Q, Wang L, Barrett P (2011) Template matching based motion classification for unsupervised post-stroke rehabilitation. In: Proceedings of ISBB, pp 199–202

  69. Zhang Z, Fang Q, Ferry F (2011) Upper limb motion capturing and classification for unsupervised stroke rehabilitation. In: Proceedings of IECON, pp 3832–3836

  70. Zhang Z, Liparulo L, Panella M, Gu X, Fang Q (2015) A fuzzy kernel motion classifier for autonomous stroke rehabilitation. IEEE J Biomed Health Inform 20(3):893–901. doi:10.1109/JBHI.2015.2430524

    PubMed  Google Scholar 

  71. Zhang Z, Ferry F, Fang Q, Gu X (2013) Robotic arm for unsupervised stroke rehabilitation: a pilot study using pid controller. In: 2013 International Conference on Orange Technologies (ICOT), pp 19–22

  72. Zhou H, Hu H, Harris N (2005) Application of wearable inertial sensors in stroke rehabilitation. In: 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005, pp 6825–6828

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184, Rome, Italy

    Luca Liparulo & Massimo Panella

  2. School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC, 3000, Australia

    Zhe Zhang & Qiang Fang

  3. Rehabilitation Medical Centre, Jiaxing 2nd Hospital, Jiaxing, 314000, Zhejiang, China

    Xudong Gu

Authors
  1. Luca Liparulo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhe Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Massimo Panella
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xudong Gu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Qiang Fang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Qiang Fang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liparulo, L., Zhang, Z., Panella, M. et al. A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography. Med Biol Eng Comput 55, 1367–1378 (2017). https://doi.org/10.1007/s11517-016-1597-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-016-1597-3

Keywords