Abstract
In needle electromyography, general mathematical methods of pattern recognition and signal analysis can be used to detect motor unit potentials and to classify various types of muscle diseases. The goal of the paper is to contribute to various methods enabling discrimination of individuals with axonal neuropathy (a positive set) from normal cases (a control set) using signals acquired from muscle activities. Data from a control set of 104 individuals and a set of 76 patients were used to validate selected methods of their separation and classification. Different features in both the time and frequency domains were studied to obtain the most reliable results . This novel approach involves comparison of individual features based on adaptive signal thresholding, as well as their combination, using supervised sigmoidal neural networks for their classification. Specificity, sensitivity and accuracy of the features used to detect individuals with axonal neuropathy was analysed using the receiver operating characteristic curves and confusion analysis. An accuracy higher than 93 % was achieved for the given sets of individuals and the optimal criterion values. The proposed modified Willison amplitude together with statistical and spectral properties of signal components classified individuals into sets of healthy and neuropathic patients by artificial neural networks with sensitivity 96.1 % and sufficient accuracy in the wide range of criterion values.
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References
Mills KR (2005) The basics of electromyography. J Neurol Neurosurg Psychiatry 76:32–35
Naik GR (2012) Computational intelligence in electromyography analysis—a perspective on current applications and future challenges. InTech
Podnar S, Vodusek DB, Stalberg E (2002) Comparison of quantitative techniques in anal sphincter electromyography. Muscle Nerve 25(1):83–92
Shapiro BE, Preston DC (2005) Electromyography and neuromuscular disorders. Butterworth–Heinemann, Newton, MA
Willison RG (1964) Analysis of electrical activity in healthy and dystrophic muscle in man. J Neurol Neurosurg Psychiatry 27:386–394
Stalberg E, Chu J, Bril V, Nandedkar S, Stalberg S, Ericsson M (1983) Automatic analysis of the EMG interference pattern. Electroencephalogr Clin Neurophysichol. 56(6):672–681
Buchta F (1991) Electromyography in the evaluation of muscle diseases. Method Clin Neurophysiol 2:25–45
Holobar A, Glaser V, Gallego JA, Dideriksen JL, Farina D (2012) Non-invasive characterization of motor unit behaviour in pathological tremor. J Neural Eng 9(5):056011 (13pp)
Major LA, Jones KE (2005) Simulations of motor unit number estimation techniques. J Neural Eng 2(2):17–34
Marateb HR, Muceli S, McGill KC, Merletti R, Farina D (2011) Robust decomposition of single-channel intramuscular EMG signals at low force levels. J Neural Eng 8(6):066015 (13pp)
Nikolic M (2001) Detailed analysis of clinical electromyography signals. Ph.D. thesis, The University of Copenhagen, the Faculty of Health Science
Raez MBI, Hussain MS, Mohd-Yasin F (2006) Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 8:11–35
Huang H, Zhang F, Sun YL, He H (2010) Design of a robust EMG sensing interface for pattern classification. J Neural Eng 7(5):056005 (10 pp)
McGill KC, Lateva ZC, Marateb HR (2005) EMGLAB: an interactive EMG decomposition program. J Neurosci Methods 149(2):121–133
Phinyomark A, Hirunviriya S, Limsakul C, Phukpattaranont P (2010) Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. In: International conference on electrical engineering/electronics computer telecommunications and information technology (ECTI-CON), pp 856–860. (Dept. of Electr. Eng., Prince of Songkla Univ., Hat Yai, Thailand, May 2010)
Tkach D, Huang H, Kuiken AT (2010) Study of stability of time-domain features for electromyographic pattern recognition. J Neuroeng Rehabil 7(21):1–13
Vaseghi S (2000) Advanced signal processing and digital noise reduction. Wiley, West Sussex
Zhou XH, Obuchowski NA, McClish DK (2002) Statistical methods in diagnostic medicine. Wiley, New York
Arikidis NS, Forster A, Abel E (2002) Interscale wavelet maximum—a fine to coarse algorithm for wavelet analysis of the EMG interference pattern. IEEE Trans Biomed Eng 49(4):337–344
Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The dual-tree complex wavelet transform. IEEE Signal Proc Mag 22:123–151
Ren X, Wang Z, Hu X (2005) Independent component analysis and wavelet decomposition technique for the detection of motor unit action potentials. In: Conf. Proc IEEE Eng. Med. Biol. Soc., pp 2687–2690
Abel EW, Forster A, Zacharia PC, Farrow TL (1996) Neural network analysis of the EMG interference pattern. Med Eng Phys 18:12–27
Finsterer J (2001) EMG-interference pattern analysis. J Electromyogr Kinesiol 11(4):231–246
Kopec J, Hausmanowa-Petrusewicz I (1985) Diagnostic yield of an automated method of quantitative electromyography. Electromyogr Clin Neurophysiol 25(7–8):567–577
Nirkko AC, Rösler KM, Hess CW (1995) Sensitivity and specificity of needle electromyography: a prospective study comparing automated interference pattern analysis with single motor unit potential analysis. Electroencephalogr Clin Neurophysiol 77(1):1–10
Subasi A (2012) Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput 12:2188–2198
Subasi A (2012) Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines. Comput Biol Med 42:806–812
Koçer S (2010) Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases. J Med Syst 34:321–329
Kaur G et al (2010) EMG diagnosis via AR modeling and binary support vector machine classification. Int J Eng Sci Technol 2(6):1767–1772
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit. Lett. 27:861–874
Leonard JA, Abel N, Cochrane T, Denys E, Goldman E, Muslick DW, Simpson D, Swisher K (2010) Guidelines for ethical behaviour related to clinical practice issues in neuromuscular and electrodiagnostic medicine. Muscle Nerve 42:480–486. https://www.aanem.org
Phinyomark A, Limsakul C, Phukpattaranont P (2011) Application of wavelet analysis in EMG feature extraction for pattern classification. Meas Sci Rev 11(2):45–52
Phinyomark A, Limsakul C, Phukpattaranont P (2009) A novel feature extraction for robust EMG pattern recognition. J Comput 1(1):71–80
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36
Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577
Kohavi R, Longbotham R, Walker T (2010) Online experiments: practical lessons. IEEE Comput 43(9):82–85
Landgrebe TCW (2008) Efficient multiclass ROC approximation by decomposition via confusion matrix perturbation analysis. IEEE Trans Pattern Anal Mach Intell 30(5):810–822
Sam V, Tai CH, Garnier J, Gibrat JF, Lee B, Munson PJ (2006) ROC and confusion analysis of structure comparison methods identify the main causes of divergence from manual protein classification. BMC Bioinformatics 7:206 (20 pp)
Klosgen W, Zytkow JM (2002) Handbook of data mining and knowledge discovery. Oxford University Press, New York
Guler NF, Kocer S (2005) Use of support vector machines and neural network in diagnosis of neuromuscular disorders. J Med Syst 29(3):271–284
AL-Allaf ONA, Tamimi AA, AbdalKader SA (2012) Artificial neural networks for iris recognition system: comparisons between different models, architectures and algorithms. Int J Inf Commun Technol Res 2(11):795–803
Bishop CM (2008) Neural networks for pattern recognition. Oxford University Press, Oxford
Yadollahi M, Prochazka A (2009) Artificial neural network in pattern recognition. In: Proceedings of the Conference on Technical Computing, pp p1–p8
Arulmozhi V (2011) Classification task by using Matlab neural network tool box a beginners view. Int J Wisdom Based Comput 1(2):59–60
Ansari S, Shafi I, Ahmad J, Shah SI (2012) Neural network-based approach for the non-invasive diagnosis and classification of hepatotropic viral disease. IET Commun 6(18):3265–3273
Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533
Sharma B, Venugopalan K (2014) Comparison of neural network training functions for Hematoma classification in brain CT images. Int J Comput Sci Eng 16(1):31–35
Karmakar P, Roy B, Paul T, Manna S (2012) Target classification: an application of artificial neural network in intelligent transport system. Int J Adv Res Comput Sci Softw Eng 2(6):242–245
Pradeep S, Srinivasu P, Avadhani PS, Murthy YVS (2011) Comparison of variable learning rate and Levenberg–Marquardt back-propagation training algorithms for detecting attacks in intrusion detection systems. Int J Comput Sci Eng 3(11):3572–3582
Kadu S, Dhande S (2012) Implementation of neural network in pattern recognization. Int J Comput Organ Trends 2(3):61–63
Kaladhar DSVGK, Rao PVN, Rajana BLVRN (2010) Confusion matrix analysis for evaluation of speech on Parkinson disease using WEKA and MATLAB. Int J Eng Sci Technol 2(10):2734–2737
Acknowledgments
Authors would like to thank all patients who signed the informed consent to participate in the project with all the procedures approved by the local ethics committee as stipulated by the Helsinki Declaration. This research was supported by a research grant from the Faculty of Chemical Engineering of the Institute of Chemical Technology in Prague, No. MSM 6046137306.
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Procházka, A., Vyšata, O., Ťupa, O. et al. Discrimination of axonal neuropathy using sensitivity and specificity statistical measures. Neural Comput & Applic 25, 1349–1358 (2014). https://doi.org/10.1007/s00521-014-1622-0
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DOI: https://doi.org/10.1007/s00521-014-1622-0