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Machine Learning in Neuromuscular Disease Classification

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Handbook of Metrology and Applications

Abstract

This work reviews the recent techniques used to analyze the EMG signals to extract and classify features. The discussed techniques are used in medical applications for the diagnosing of neuromuscular disorders. Techniques such as wavelet transform (WT), principal component analysis (PCA), empirical mode decomposition (EMD), artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), k-nearest neighbors (kNN), and deep learning are among the powerful techniques used for feature extraction and disease classification. The many improvements made to the algorithms could increase the performance and accuracy of the models discussed in this area. Recently, the researchers focused their attention on the classification of neuromuscular illnesses using artificial intelligence to acquire and classify myoelectric signals via electromyography (EMG). Amyotrophic lateral sclerosis (ALS) is one of the neuromuscular illnesses that drives the attention of the researchers in this field. The current work focuses on the most recent methods for extracting and categorizing characteristics from EMG data to diagnose neuromuscular disorders in medical applications. Feature extraction approaches presented here include the wavelet transform (WT), principal component analysis (PCA), and empirical mode decomposition (EMD). Artificial neural networks include artificial neural networks (ANNs), support vector machines (SVM), extreme learning machines (ELM), k-nearest neighbors (kNN), and deep learning. An example on the uncertainty evaluation in machine learning classification is introduced.

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References

  • AbdelMaboud NF, Elbagoury B, Roushdy M, Salem AM (2015) A new hybrid classifier for neuromuscular disorders diagnoses. Egypt Comput Sci J (ECS) 39:86–92

    Google Scholar 

  • Abdel-Maboud NF, Tawfik MA, Parusheva SS, Salem AM (2022) Advances of machine learning in electromyography (EMG) signal classification. World J Eng Res Technol 8(2):1–20

    Google Scholar 

  • Alaskar H (2018) Convolutional neural network application in biomedical signals. J Comput Sci Inf Technol 6(2):45–59. https://doi.org/10.15640/jcsit.v6n2a5

    Article  Google Scholar 

  • Alzaq H, ÃœstündaÄŸ BB (2018) A comparative performance of discrete wavelet transform implementations using multiplierless. In: Wavelet theory and its applications. IntechOpen. https://doi.org/10.5772/intechopen.76522

    Chapter  Google Scholar 

  • Artameeyanant P, Sultornsanee S, Chamnongthai K (2016) An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection. Springer Plus 5(1):1–26. https://doi.org/10.1186/s40064-016-3772-2

    Article  Google Scholar 

  • Balli T, Palaniappan R (2009) Minimising prediction error for optimal nonlinear modelling of EEG signals using genetic algorithm. In: Proceedings of the 4th international IEEE EMBS conference on neural engineering, Antalya, Turkey, pp 364–366

    Google Scholar 

  • Berger A, Nascimento FA, Carmo JC, Rocha AF (2006) Compression of EMG signals with wavelet transform and artificial neural networks. Physiol Meas 27(6):457–465. https://doi.org/10.1088/0967-3334/27/6/003. Epub 2006 Mar 22

    Article  Google Scholar 

  • Bhuvaneswari P, Kumar JS (2016) Electromyography based detection of neuropathy disorder using reduced cepstral feature. Indian J Sci Technol 9(8):1–4. https://doi.org/10.17485/ijst/2016/v9i8/87899

    Article  Google Scholar 

  • Bozkurt MR, SubaÅŸi A, Köklükaya E, Yilmaz M (2016) Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models. Turk J Electr Eng Comput Sci 24:1547–1559. https://doi.org/10.3906/elk-1309-1

    Article  Google Scholar 

  • Cura OK, Atli SK, Türe HS, Akan A (2020) Epileptic seizure classifications using empirical mode decomposition and its derivative. Bio Med Eng OnLine 19(10). https://doi.org/10.1186/s12938-020-0754-y

  • Elamvazuthi I, Duy N, Ali Z, Su SW, Khan M, Parasuraman S (2015) Electromyography (EMG) based classification of neuromuscular disorders using multi-layer perceptron. Procedia Comput Sci 76:223–228. https://doi.org/10.1016/j.procs.2015.12.346

    Article  Google Scholar 

  • Ene M (2008) Neural network-basedaApproach to discriminate healthy people from those with parkinson’s disease. Math Comput Sci Ser 35:112–116

    MATH  Google Scholar 

  • Fattah SA, Doulah U, Iqbal MA, Shahnaz C, Zhu W, Ahmad MO (2013) Identification of motor neuron disease using wavelet domain features extracted from EMG signal. IEEE Int Symp Circuits Syst (ISCAS):1308–1311. https://doi.org/10.1109/iscas.2013.6572094

  • Garrone R (2020) Naïve principal component analysis. https://www.bamboos-consulting.com/blog/naive-principal-component-analysis/

  • Goen A (2014) Classification of EMG signals for assessment of neuromuscular disorders. Int J Electron Electr Eng 2:242–248. https://doi.org/10.12720/ijeee.2.3.242-248

    Article  Google Scholar 

  • Goldberger L, Amaral A, Glass L, Hausdorff M, Ivanov C, Mark G, Mietus E, Moody B, Peng K, Stanley E (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 101(23):E215–20. https://doi.org/10.1161/01.cir.101.23.e215. PMID: 10851218.

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. J Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  • Illias HA, Chai XR, Abu Bakar A, Mokhlis H (2015) Flowchart of ANN algorithm. Plos One. Figure. https://doi.org/10.1371/journal.pone.0129363.g001

  • Kehri V, Ingle R, Awale R, Oimbe S (2017) Techniques of EMG signal analysis and classification of neuromuscular diseases. Adv Intell Syst Res 137:485–491. https://doi.org/10.2991/iccasp-16.2017.71

    Article  Google Scholar 

  • Khan MT, Hasan MT (2015) Comparison between kNN and SVM for EMG signal classification. Int J Recent Innov Trends Comput Commun (IJRITCC) 3(12):6799–6801

    Google Scholar 

  • Khan M, Singh J, Tiwari M (2016) A multi-classifier approach of EMG signal classification for diagnosis of neuromuscular disorders. J Bioeng Biomed Sci 133(4):13–18. https://doi.org/10.5120/ijca2016907710

    Article  Google Scholar 

  • Kiran PU, Abhiram N, Taran S, Bajaj V (2018) TQWT based features for classification of ALS and healthy EMG signals. Am J Comput Sci Inf Technol 6(2). https://doi.org/10.21767/2349-3917.100019

  • Kordylewski H, Graupe D, Liu K (2001) A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Trans Inform Technol Biomed 5(3):202–209

    Article  Google Scholar 

  • Lachish S, Murray K (2018) The certainty of uncertainty: potential sources of bias and imprecision in disease ecology studies. Front Vet Sci. https://www.frontiersin.org/articles/10.3389/fvets.2018.00090/full

  • Lanyi X, Adler A (2004) An improved method for muscle activation detection during gait. Can Conf Electr Comput Eng 1:357–360

    Google Scholar 

  • Mannini A, Trojaniello D, Cereatti A, Sabatini A (2016) A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and huntington’s disease patients. Sensors 16(1134):1–14. https://doi.org/10.3390/s16010134

    Article  Google Scholar 

  • McCool P, Fraser GD, Chan AD, Petropoulakis L, Soraghan JJ (2014) Identification of contaminant type in surface electromyography (EMG) signals. IEEE Trans Neural Syst Rehabil Eng 22(4):774–783. https://doi.org/10.1109/TNSRE.2014.2299573. Epub 2014 Jan 21

    Article  Google Scholar 

  • Meena P, Bansal M (2016) Classification of EMG signals using SVM-kNN. Int J Adv Res Electr Commun Eng (IJARECE) 5(6):1718–1724

    Google Scholar 

  • Mierswa I, Wurst M, Klinkenberg R, Scholz M, Yal E (2006) Rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. https://doi.org/10.1145/1150402.1150531

  • Niaf E, Flamary R, Lartizien C, Cany S (2011) Handling uncertainties in SVM classification. In: Proceedings of IEEE Workshop Stat Signal Proc, 2011, pp 757–760

    Google Scholar 

  • Nikolic M (2001) Detailed analysis of clinical electromyography signals EMG decomposition, findings and firing pattern analysis in controls and patients with myopathy and amytrophic lateral sclerosis. PhD Thesis, Faculty of Health Science, University of Copenhagen, 2001. [The data are available as dataset N2001 at http://www.emglab.net]

  • Pathmanathan P, Galappaththige SK, Cordeiro JM, Kaboudian A, Fenton FH, Gray RA (2020) Data-driven uncertainty quantification for cardiac electrophysiological models: impact of physiological variability on action potential and spiral wave dynamics. Front Physiol. https://www.frontiersin.org/articles/10.3389/fphys.2020.585400/full

  • Priyadharsini S, Sonia B, Dejey D (2015) A hybrid ELM-wavelet technique for the classification and diagnosis of neuromuscular disorder using EMG signal. Inst Integr Omics Appl Biotechnol J (IIOABJ) 6:98–106

    Google Scholar 

  • Raez M (2006) Techniques of EMG signal analysis: detection, classification and applications. Biol Proced Online 8:11–35

    Article  Google Scholar 

  • Saxena R (2016) KNN classifier, introduction to K-nearest neighbor algorithm. dataaspirant.com/k-nearest-neighbor-classifier-intro/

  • Sengur A, Akbulut Y, Guo Y, Bajaj V (2017) Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm. Health Inf Sci Syst 5(1):5–9. https://doi.org/10.1007/s13755-017-0029-6

    Article  Google Scholar 

  • Sharma LN, Dandapat S, Mahanta A (2010) Multi-scale principal component analysis for multichannel ECG data reduction. In: Proceedings of the 10th IEEE international conference on information technology and applications in biomedical, pp 1–4. https://doi.org/10.1109/ITAB.2010.5687778

  • Shaw L, Bagha S (2012) Online EMG signal analysis for diagnosis of neuromuscular diseases by using PCA and PNN. Int J Eng Sci Technol (IJEST) 4:4453–4459

    Google Scholar 

  • Shijiya S, Thomas P (2016) An improved method to detect common muscular disorders from EMG signals using artificial neural network and fuzzy logic. Int J Adv Technol Eng Sci (IJATES) 4(7):68–75

    Google Scholar 

  • Singh AK, Agrawal NK, Gupta S (2017) Approach for classification of neuromuscular disorder using EMG signals. Int J Innov Res Comput Commun Eng (IJIRCCE) 5(5):9382–9387

    Google Scholar 

  • Subasi A (2015) A decision support system for diagnosis of neuromuscular disorders using DWT and evolutionary support vector machines. SIViP 9:399–408. https://doi.org/10.1007/s11760-013-0480-z

    Article  Google Scholar 

  • Subasi A, Yaman E, Somaily Y, Alynabawi HA, Alobaidi F, Altheibani S (2018) Automated EMG signal classification for diagnosis of neuromuscular disorders using DWT and bagging. Procedia Comput Sci 140:230–237. https://doi.org/10.1016/j.procs.2018.10.333

    Article  Google Scholar 

  • SzkoÅ‚a J, Pancerz K, WarchoÅ‚ J (2011) Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study. Neuroscience 2(28):93–98. https://doi.org/10.1155/2011/289398

    Article  Google Scholar 

  • Vallejo M, Espriella C, Santamaría J, Barrera A, Trejos E (2020) Soft metrology based on machine learning: a review. Meas Sci Technol 31(3). https://doi.org/10.1088/1361-6501/ab4b39

  • Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Elsevier. https://doi.org/10.1016/C2009-0-19715-5

    Book  Google Scholar 

  • Zhou ZH (2012) Ensemble methods: foundations and algorithms, 1st edn. Chapman and Hall, CRC Press. https://doi.org/10.1201/b12207

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Author information

Authors and Affiliations

  1. Length and Engineering Precision, National Institute of Standards, Giza, Egypt

    Niveen Farid

Authors
  1. Niveen Farid
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Editor information

Editors and Affiliations

  1. Bhabha Atomic Research Centre, Mumbai, Maharashtra, India

    Dinesh K. Aswal

  2. Physico-Mechanical Metrology, CSIR - National Physical Laboratory, New Delhi, Delhi, India

    Sanjay Yadav

  3. National Metrology Institute of Japan, National Institute of Advanced Industrial, Ibaraki, Japan

    Toshiyuki Takatsuji

  4. National Institute of Standards and Tech, Gaithersburg, MD, USA

    Prem Rachakonda

  5. Mechanical Engineering Department, National Institute of Technology, Delhi, India

    Harish Kumar

Section Editor information

  1. Mechanical Engineering Department, National Institute of Technology (NIT), Delhi, India

    Harish Kumar

  2. M M University Ambala, Ambala, India

    Dharambir Singh PhD

  3. CSIR-National Physical Laboratory, New Delhi, India

    P. K Dubey PhD

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Farid, N. (2023). Machine Learning in Neuromuscular Disease Classification. In: Aswal, D.K., Yadav, S., Takatsuji, T., Rachakonda, P., Kumar, H. (eds) Handbook of Metrology and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-99-2074-7_56

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