Skip to main content

Advertisement

SpringerLink
Log in

Deep neural network assisted diagnosis of time-frequency transformed electromyograms

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Electromyograms (EMG) are recorded electrical signals generated from the muscles and these signals are closely interrelated with the muscle activity and hence are useful for the investigation of neuro-muscular disorders. The feature mining, feature collection and development of classification systems are greatly significant steps in the differentiation of normal and abnormal EMG signals to evaluate the abnormality. In this work, time-frequency domain based features of regular, myopathy and Amyotrophic Lateral Sclerosis (ALS) EMG signals were extracted from four different techniques namely Stockwell-Transform (ST), Wigner-Ville Transform (WVT), Synchro-Extracting Transform (SET) and Short-Time Fourier Transform (STFT). The Particle Swarm Optimization (PSO) with fractional velocity update technique was implemented for feature reduction. Further, the classifier based on the Deep Neural Networks (DNN) was developed by employing the features selected using fractional PSO. Finally, the performance of the DNN was compared with that of the Shallow Neural Network (SNN) classifier. Results of this work demonstrate that, the performance measure of the DNN classifiers is higher than that of the SNN classifier. This work appears to be of good clinical significance since efficient classification techniques are required for the development of robust neuro-muscular diagnosis systems.

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

Access this article

Log in via an institution

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Alagumariappan P, Krishnamurthy K (2018) An Approach Based on Information Theory for Selection of Systems for Efficient Recording of Electrogastrograms. In Proceedings of the International Conference on Computing and Communication Systems (pp. 463–471). Springer, Singapore

  2. Alagumariappan P, Rajagopal A, Krishnamurthy K (2016) Complexity Analysis on Normal and Abnormal Electrogastrograms Using Tsallis Entropy. In 3rd International Electronic and Flipped Conference on Entropy and Its Applications. Multidisciplinary Digital Publishing Institute

  3. Alagumariappan P, Krishnamurthy K, Kandiah S, Ponnuswamy MJ (2017) Effect of electrode contact area on the information content of the recorded electrogastrograms: an analysis based on Rényi entropy and Teager-Kaiser energy. Polish Journal of Medical Physics and. Engineering 23(2):37–42

    Google Scholar 

  4. Al-Barazanchi KK, Al-Neami AQ, Al-Timemy AH (2017). Ensemble of bagged tree classifier for the diagnosis of neuromuscular disorders. In Advances in Biomedical Engineering (ICABME), 2017 Fourth International Conference on (pp. 1–4). IEEE

  5. Ambikapathy B, Krishnamurthy K (2018) Analysis of electromyograms recorded using invasive and noninvasive electrodes: a study based on entropy and Lyapunov exponents estimated using artificial neural networks. J Ambient Intel Humanized Comput 1–9

  6. Amin M, Cohen L, Williams WJ (1993). Methods and applications for time frequency analysis. In Conference Notes, University of Michigan

  7. Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.04.065

  8. Ansari GJ, Shah JH, Yasmin M, Sharif M, Fernandes SL (2018) A novel machine learning approach for scene text extraction. Future Gen Comput Syst

  9. Arunkumar N, Ramkumar K, Venkatraman V, Abdulhay E, Fernandes SL, Kadry S, Segal S (2017) Classification of focal and non focal EEG using entropies. Pattern Recogn Lett 94:112–117

    Article  Google Scholar 

  10. Belkhou A, Jbari A, Belarbi L (2017) A continuous wavelet based technique for the analysis of electromyography signals. In Electrical and Information Technologies (ICEIT), 2017 International Conference on (pp. 1–5). IEEE

  11. Boashash B (1991) Time-frequency signal analysis. Prentice Hall

  12. Chandra B, Sharma RK (2016) Fast learning in deep neural networks. Neurocomputing 171:1205–1215

    Article  Google Scholar 

  13. Christodoulou CI, Pattichis CS (1999) Unsupervised pattern recognition for the classification of EMG signals. IEEE Trans Biomed Eng 46(2):169–178

    Article  Google Scholar 

  14. Duque CJG, Muñoz LD, Mejía JG, Trejos ED (2014). Discrete wavelet transform and k-nn classification in EMG signals for diagnosis of neuromuscular disorders. In Image, Signal Processing and Artificial Vision (STSIVA), 2014 XIX Symposium on (pp. 1–5). IEEE

  15. Daud WMBW, Yahya AB, Horng CS, Sulaima MF, Sudirman R (2013) Features extraction of electromyography signals in time domain on biceps Brachii muscle. Int J Model Opt 3(6):515

    Google Scholar 

  16. Davies MR, Reisman SS (1994) Time frequency analysis of the electromyogram during fatigue. In Bioengineering Conference, 1994., Proceedings of the 1994 20th Annual Northeast (pp. 93–95). IEEE

  17. Fernandes SL, Chakraborty B, Gurupur VP, Prabhu G (2016) Early skin cancer detection using computer aided diagnosis techniques. J Integr Des Process Sci 20(1):33–43

    Article  Google Scholar 

  18. Fernandes SL, Gurupur VP, Lin H, Martis RJ (2017) A novel fusion approach for early lung Cancer detection using computer aided diagnosis techniques. J Med Imaging Health Inform 7(8):1841–1850

    Article  Google Scholar 

  19. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man, Cybernet 3(6):610–621

    Article  Google Scholar 

  20. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    Article  Google Scholar 

  21. Jang GC, Cheng CK, Lai JS, Kuo TS (1994) Using time-frequency analysis technique in the classification of surface EMG signals. In Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE (Vol. 2, pp. 1242–1243). IEEE

  22. Kamalanand K, Jawahar PM (2012) Coupled jumping frogs/particle swarm optimization for estimating the parameters of three dimensional HIV model. BMC Infect Dis 12(1):P82

    Article  Google Scholar 

  23. Kamalanand K, Jawahar PM (2013a) Particle swarm optimization based estimation of HIV-1 viral load in resource limited settings. Afr J Microbiol Res 7(20):2297–2304

    Article  Google Scholar 

  24. Kamalanand K, Jawahar PM (2014b) Hybrid BFPSO algorithm based estimation of optimal drug dosage for antiretroviral therapy in HIV-1 infected patients. BMC Infect Dis 14(S3):E14

    Article  Google Scholar 

  25. Kamalanand K, Mannar Jawahar P (2015) Comparison of swarm intelligence techniques for estimation of HIV-1 viral load. IETE Tech Rev 32(3):188–195

    Article  Google Scholar 

  26. Kamalanand K, Mannar Jawahar P (2016) Comparison of particle swarm and bacterial foraging optimization algorithms for therapy planning in HIV/AIDS patients. Int J Biomath 9(02):1650024

    Article  MathSciNet  Google Scholar 

  27. Karthick PA, Ghosh DM, Ramakrishnan S (2018) Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Comput Methods Prog Biomed 154:45–56

    Article  Google Scholar 

  28. Kuniszyk-Józkowiak W, Jaszczuk J, Sacewicz T, Codello I (2012). Time-frequency Analysis of the EMG Digital Signals. In Annales UniversitatisMariae Curie-Sklodowska (Vol. 12, No. 2, p. 19). De Gruyter Open Sp. z oo

  29. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  30. Manickavasagam K, Sutha S, Kamalanand K (2014) Development of systems for classification of different plasmodium species in thin blood smear microscopic images. J Adv Microsc Res 9(2):86–92

    Article  Google Scholar 

  31. 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. [The data are available as dataset N2001 at http://www.emglab.net]

  32. Nuwer MR, Comi G, Emerson R, Fuglsang-Frederiksen A, Guerit JM, Hinrichs H et al (1999) IFCN standards for digital recording of clinical EEG. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl 52:11

    Google Scholar 

  33. Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39(8):7420–7431

    Article  Google Scholar 

  34. Pires ES, Machado JT, de Moura Oliveira PB, Cunha JB, Mendes L (2010) Particle swarm optimization with fractional-order velocity. Nonlinear Dynamics 61(1–2):295–301

    Article  Google Scholar 

  35. Quynh TL, Ardi HA, Gilat M, Rifai C, Ehgoetz MK, Georgiades M, … Nguyen HT (2017). Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. In Conference proceedings:... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference (Vol. 2017, p. 3044)

  36. Raja NSM, Rajinikanth V, Fernandes SL, Satapathy SC (2017) Segmentation of breast thermal images using Kapur's entropy and hidden Markov random field. J Med Imaging Health Inform 7(8):1825–1829

    Article  Google Scholar 

  37. Raja NSM, Fernandes SL, Dey N, Satapathy SC, Rajinikanth V (2018) Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Journal of Ambient Intelligence and Humanized Computing, 1–12

  38. Rajagopal A, Alagumariappan P, Krishnamurthy K (2018) Development of an Automated Decision Support System for Diagnosis of Digestive Disorders Using Electrogastrograms: An Approach Based on Empirical Mode Decomposition and K-Means Algorithm. In Expert System Techniques in Biomedical Science Practice (pp. 97–119). IGI Global

  39. Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images–a study with teaching learning based optimization. Pattern Recogn Lett 94:87–94

    Article  Google Scholar 

  40. Rajinikanth V, Raja NSM, Satapathy SC, Fernandes SL (2017) Otsu’s multi-thresholding and active contour snake model to segment dermoscopy images. J Med Imaging Health Inform 7(8):1837–1840

    Article  Google Scholar 

  41. Rajinikanth V, Raja NSM, Kamalanand K (2017) Firefly algorithm assisted segmentation of tumor from brain MRI using Tsallis function and Markov random field. J Control Eng Appl Inform 19(2):97–106

    Google Scholar 

  42. Rajinikanth V, Dey N, Satapathy SC, Ashour AS (2018) An approach to examine magnetic resonance angiography based on Tsallis entropy and deformable snake model. Futur Gener Comput Syst 85:160–172

    Article  Google Scholar 

  43. Ricamato AL, Absher RG, Moffroid MT, Tranowski JP (1992). A time-frequency approach to evaluate electromyographic recordings. In Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on (pp. 520–527). IEEE

  44. Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL (2018) A framework for offline signature verification system: best features selection approach. Pattern Recogn Lett

  45. Sharma S, Farooq H, Chahal N. Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation

  46. Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001

    Article  Google Scholar 

  47. Subasi A (2012) Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput 12(8):2188–2198

    Article  Google Scholar 

  48. Wang G, Zhang Y, Wang J (2014) The analysis of surface emg signals with the wavelet-based correlation dimension method. Comput Math Methods Med

  49. Yousefi J, Hamilton-Wright A (2014) Characterizing EMG data using machine-learning tools. Comput Biol Med 51:1–13

    Article  Google Scholar 

  50. Yu G, Yu M, Xu C (2017) Synchroextracting Transform. IEEE Transactions on Industrial Electronics

Download references

Author information

Authors and Affiliations

  1. Department of Instrumentation Engineering, MIT campus, Anna University, Chennai, India

    A. Bakiya & K. Kamalanand

  2. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, India

    V. Rajinikanth

  3. Department of Information Science & Engineering Department, Canara Engineering College, Mangaluru, Karnataka, India

    Ramesh Sunder Nayak

  4. Department of Mathematics and Computer Science, Beirut Arab University, Beirut, Lebanon

    Seifedine Kadry

Authors
  1. A. Bakiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Kamalanand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. Rajinikanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ramesh Sunder Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seifedine Kadry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Rajinikanth.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bakiya, A., Kamalanand, K., Rajinikanth, V. et al. Deep neural network assisted diagnosis of time-frequency transformed electromyograms. Multimed Tools Appl 79, 11051–11067 (2020). https://doi.org/10.1007/s11042-018-6561-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6561-9

Keywords

Search

Navigation