Classification of electromyography signals using relevance vector machines and fractal dimension

详细信息    查看全文

• 作者：Clodoaldo A. M. Lima ; André L. V. Coelho…
• 关键词：EMG signal classification ; Relevance vector machines ; Fractal dimension ; Feature extraction
• 刊名：Neural Computing & Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：27
• 期：3
• 页码：791-804
• 全文大小：1,308 KB
• 参考文献：1.Abry P, Gonçalves P, Véhel JL (eds) (2013) Scaling, fractals and wavelets. Wiley, New YorkMATH
  2.Acharya UR, Ng EYK, Swapna G, Michelle YSL (2011) Classification of normal, neuropathic, and myopathic electromyograph signals using nonlinear dynamics method. J Med Imag Health Inform 1:375–380CrossRef
  3.Ancillao A, Galli M, Rigoldi C, Albertini G (2014) Linear correlation between fractal dimension of surface EMG signal from rectus femoris and height of vertical jump. Chaos Solitons Fractals 66:120–126CrossRef
  4.Barnsley M (1988) Fractals everywhere. Academic Press, New YorkMATH
  5.Bishop CM (2006) Pattern recognition and machine learning. Springer, BerlinMATH
  6.Chan AD, Green GC (2007) Myoelectric control development toolbox. In: Proceedings of 30th conference of the Canadian medical & biological engineering society
  7.Chan FH, Yang YS, Lam FK, Zhang YT, Parker PA (2000) Fuzzy EMG classification for prosthesis control. IEEE Trans Rehabil Eng 8:305–311CrossRef
  8.Chang GC, Kang WJ, Luh JJ, Cheng CK, Lai JS, Chen JJJ, Kuo TS (1996) Real-time implementation of electromyogram pattern recognition as a control command of man–machine interface. Med Eng Phys 18(7):529–537CrossRef
  9.Chu JU, Moon I, Lee YJ, Kim SK, Mun MS (2007) A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE-ASME Trans Mechatron 12:282–290CrossRef
  10.Damoulas T, Girolami M, Ying Y, Campbell C (2008) Inferring sparse kernel combinations and relevance vectors: An application to subcellular localization of proteins. In: Proceedings of the 7th International Conference in Machine Learning Applications, pp 577–582
  11.Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNet MATH
  12.Dobrowolski AP, Wierzbowski M, Tomczykiewicz K (2012) Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders. Comput Meth Prog Bio 107:393–403CrossRef
  13.Easwaramoorthy D, Uthayakumar R (2011) Improved generalized fractal dimensions in the discrimination between healthy and epileptic EEG signals. J Comput Sci 2:31–38CrossRef
  14.Eke A, Herman P, Kocsis L, Kozak LR (2002) Fractal characterization of complexity in temporal physiological signals. Physiol Meas 23:1–38CrossRef
  15.Englehart K, Hudgins B, Chan ADC (2003) Continuous multifunction myoelectric control using pattern recognition. Technol Disabil 15(2):95–103
  16.Englehart K, Hudgins B, Parker P (2001) A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 48(3):302–311CrossRef
  17.Goge A, Chan A (2004) Investigating classification parameters for continuous myoelectrically controlled prostheses. In: Proceedings of the 28th conference of the Canadian medical & biological engineering society, pp 141–144
  18.He W, Yow KC, Guo Y (2012) Recognition of human activities using a multiclass relevance vector machine. Opt Eng 51:017,202CrossRef
  19.Higuchi T (1988) Approach to irregular time series on the basis of the fractal theory. Phys D 31(2):277–283MathSciNet CrossRef MATH
  20.Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley, New YorkMATH
  21.Hu X, Wang Z, Ren X (2005) Classification of surface EMG signal using relative wavelet packet energy. Comput Meth Prog Biomed 79:189–195CrossRef
  22.Hu X, Wang ZZ, Ren XM (2005) Classification of surface EMG signal with fractal dimension. J Zhejiang Univ Sci B 6:844–848CrossRef
  23.Huang H, Xie HB, Guo JY, Chen HJ (2012) Ant colony optimization-based feature selection method for surface electromyography signals classification. Comput Biol Med 42:30–38CrossRef
  24.Hudgins B, Parker P, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40(1):82–94CrossRef
  25.Janjarasjitt S (2014) Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data. J Zhejiang Univ Sci C 15:1147–1153CrossRef
  26.Kang WJ, Cheng CK, Lai JS, Shiu JR, Kuo TS (1996) A comparative analysis of various EMG pattern recognition methods. Med Eng Phys 18(5):390–395CrossRef
  27.Katz M (1988) Fractals and the analysis of waveforms. Comput Biol Med 18(3):145–156CrossRef
  28.Khokhar ZO, Xiao ZG, Menon C (2010) Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomed Eng Online 9:41CrossRef
  29.Lima CAM, Coelho ALV (2011) Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study. Artif Intell Med 53:83–95CrossRef
  30.Lima CAM, Coelho ALV, Chagas S (2009) Automatic EEG signal classification for epilepsy diagnosis with relevance vector machines. Expert Syst Appl 36:10054–10059CrossRef
  31.Lucas MF, Gaufriau A, Pascual S, Doncarli C, Farina D (2008) Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization. Biomed Signal Proces Control 3:169–174CrossRef
  32.Najarian K, Splinter R (2012) Biomedical signal and image processing, 2nd edn. CRC Press, Boca Raton
  33.Nussbaum M A, Yassierli (2003) Assessment of localized muscle fatigue furing low-moderate static contractions using the fractal dimension of EMG. In: Proceedings of the XVth triennial congress of the international ergonomics association, Seoul, Korea, August 25–29
  34.Phinyomark A, Phukpattaranont P, Limsakul C (2012) Fractal analysis features for weak and single-channel upper-limb EMG signals. Expert Syst Appl 39:11156–11163CrossRef
  35.Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y (2014) Feature extraction of the first difference of EMG time series for EMG pattern recognition. Comput Methods Programs Biomed 117:247–256CrossRef
  36.Psorakis I, Damoulas T, Girolami MA (2010) Multiclass relevance vector machines: sparsity and accuracy. IEEE Trans Neural Netw 21(10):1588–1598CrossRef
  37.Riillo F, Quitadamo L, Cavrinia F, Gruppioni E, Pinto C, Pastò NC, Sbernini L, Albero L, Saggio G (2014) Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees. Biomed Signal Process Control 14:117–125CrossRef
  38.Sarkar M, Leong TY (2003) Characterization of medical time series using fuzzy similarity-based fractal dimensions. Artif Intell Med 27:201–222CrossRef
  39.Scholköpf B, Smola A (2002) Learning with kernels. MIT Press, CambridgeMATH
  40.Sevcik C (1998) A procedure to estimate the fractal dimension of waveforms. Complex Int 5:1–19MathSciNet MATH
  41.Subasi A (2013) Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43:576–586CrossRef
  42.Tipping M (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244MathSciNet MATH
  43.Tipping M, Faul A (2003) Fast marginal likelihood maximisation for sparse bayesian models. In: Proceedings of 9th AISTATS workshop, pp 3–6
  44.Tricot C (1995) Curves and Fractal Dimension. Springer, New YorkCrossRef MATH
  45.Yana Z, Wanga Z, Xieb H (2008) The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification. Comput Meth Prog Biomed 90:275–284CrossRef
  46.Yousefi J, Hamilton-Wright A (2014) Characterizing EMG data using machine-learning tools. Comput Biol Med 51:1–13CrossRef
  
• 作者单位：Clodoaldo A. M. Lima (1)
  André L. V. Coelho (2)
  Renata C. B. Madeo (1)
  Sarajane M. Peres (1)
  
  1. Information Systems Program, School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil
  2. Graduate Program in Applied Informatics, Center of Technological Sciences, University of Fortaleza, Fortaleza, Brazil
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

Surface electromyography (EMG) signals have been studied extensively in the last years aiming at the automatic classification of hand gestures and movements as well as the early identification of latent neuromuscular disorders. In this paper, we investigate the potentials of the conjoint use of relevance vector machines (RVM) and fractal dimension (FD) for automatically identifying EMG signals related to different classes of limb motion. The adoption of FD as the mechanism for feature extraction is justified by the fact that EMG signals usually show traces of self-similarity. In particular, four well-known FD estimation methods, namely box-counting, Higuchi’s, Katz’s and Sevcik’s methods, have been considered in this study. With respect to RVM, besides the standard formulation for binary classification, we also investigate the performance of two recently proposed variants, namely constructive mRVM and top-down mRVM, that deal specifically with multiclass problems. These classifiers operate solely over the features extracted by the FD estimation methods, and since the number of such features is relatively small, the efficiency of the classifier induction process is ensured. Results of experiments conducted on a publicly available dataset involving seven distinct types of limb motions are reported whereby we assess the performance of different configurations of the proposed RVM+FD approach. Overall, the results evidence that kernel machines equipped with the FD feature values can be useful for achieving good levels of classification performance. In particular, we have empirically observed that the features extracted by the Katz’s method is of better quality than the features generated by other methods.