Classification of Cervical Disc Herniation Disease using Muscle Fatigue Based Surface EMG Signals by Artificial Neural Networks

Authors

  • Guzin Ozmen Selcuk University
  • Ahmet Hakan Ekmekci Selcuk University

DOI:

https://doi.org/10.18201/ijisae.2017533901

Keywords:

ANN, AR, Surface Electromyography, Muscle Fatigue, STFT, DWT

Abstract

This study presents the classification of cervical disc herniation patient and healthy persons by using muscle fatigue information. Cervical disc herniation patients suffer from neck pain and muscle fatigue in the neck increases these aches. Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this study surface Electromyography (EMG) signals were used to examine muscle fatigue. EMG signals were obtained from Trapezius and Sternocleidomastoid (SCM) muscles in the cervical region of 10 control subject and 10 cervical disc herniation patients. Surface EMG was preferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue was determined using Median and Mean frequency values obtained by Fourier Transform and Welch methods. Feature extraction was the third step which was performed by Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR).  Finally, Artificial Neural Network (ANN) was used for classification. Training and test data were created by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigated on patient classification using muscle fatigue information. The best results were obtained by AR method with %99 classification accuracy.  Also, the best results were obtained by DWT with %100 classification accuracy for SCM muscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMG signals by providing high accuracy classification with artificial intelligence methods for cervical disc herniation disease. Besides, it is shown that muscle fatigue distinguishes cervical disc herniation patients from healthy people.

Downloads

Download data is not yet available.

References

J. D. Bronzino, Biomedical engineering handbook. CRC press, 1999.

J. Kimura, Electrodiagnosis in diseases of nerve and muscle: principles and practice. Oxford university press, 2001.

K. Masuda, T. Masuda, T. Sadoyama, M. Inaki, and S. Katsuta, "Changes in surface EMG parameters during static and dynamic fatiguing contractions," Journal of electromyography and kinesiology, vol. 9, no. 1, pp. 39-46, 1999.

C. J. De Luca, A. Adam, R. Wotiz, L. D. Gilmore, and S. H. Nawab, "Decomposition of surface EMG signals," Journal of neurophysiology, vol. 96, no. 3, pp. 1646-1657, 2006.

J. Duchene, D. Devedeux, S. Mansour, and C. Marque, "Analyzing uterine EMG: tracking instantaneous burst frequency," IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 2, pp. 125-132, 1995.

P. Bonato, G. Gagliati, and M. Knaflitz, "Analysis of myoelectric signals recorded during dynamic contractions," IEEE engineering in medicine and biology magazine, vol. 15, no. 6, pp. 102-111, 1996.

S. Karlsson, J. Yu, and M. Akay, "Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study," IEEE transactions on Biomedical Engineering, vol. 47, no. 2, pp. 228-238, 2000.

J. Karlsson, B. Gerdle, and M. Akay, "Analyzing surface myoelectric signals recorded during isokinetic contractions," IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 6, pp. 97-105, 2001.

H. Biedermann, G. Shanks, W. Forrest, and J. Inglis, "Power Spectrum Analyses of Electromyographic Activity: Discriminators in the Differential Assessment of Patients with Chronic Low-Back Pain," Spine, vol. 16, no. 10, pp. 1179-1184, 1991.

A. Luttmann, M. Jāger, J. Sökeland, And W. Laurig, "Electromyographical study on surgeons in urology. II. Determination of muscular fatigue," Ergonomics, vol. 39, no. 2, pp. 298-313, 1996.

P. Konrad, "The abc of emg," A practical introduction to kinesiological electromyography, vol. 1, pp. 30-35, 2005.

G. Kim, M. A. Ahad, M. Ferdjallah, and G. F. Harris, "Correlation of muscle fatigue indices between intramuscular and surface EMG signals," in SoutheastCon, 2007. Proceedings. IEEE, 2007, pp. 378-382: IEEE.

L. H. Lindstrom and R. I. Magnusson, "Interpretation of myoelectric power spectra: a model and its applications," Proceedings of the IEEE, vol. 65, no. 5, pp. 653-662, 1977.

C. U. Ranniger and D. L. Akin, "EMG mean power frequency determination using wavelet analysis," in Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, 1997, vol. 4, pp. 1589-1592: IEEE.

H. Xie and Z. Wang, "Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis," Computer methods and programs in biomedicine, vol. 82, no. 2, pp. 114-120, 2006.

B. Boashash and A. Reilly, Algorithms for time-frequency signal analysis. Longman Cheshire, 1992.

D. Farina and R. Merletti, "Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions," Journal of Electromyography and Kinesiology, vol. 10, no. 5, pp. 337-349, 2000.

Z. Moussavi, J. Cooper, and E. Shwedyk, "Fatigue pattern of trapezius muscle in relation to its functional role," in Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE, 1997, vol. 4, pp. 1451-1453: IEEE.

L. McLean, R. Scott, and J. Rickards, "Measurement of muscle fatigue in the cervical and lumbar regions during prolonged sitting," in Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, 1997, vol. 4, pp. 1659-1662: IEEE.

A. Subasi and M. K. Kiymik, "Muscle fatigue detection in EMG using time–frequency methods, ICA and neural networks," Journal of medical systems, vol. 34, no. 4, pp. 777-785, 2010.

G. Özmen, "Servikal bölgede oluşan kas yorgunluğunun yüzey elektromiyogram bilgileri ile değerlendirilmesi," Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2013.

M. R. Al-Mulla, F. Sepulveda, and M. Colley, "A review of non-invasive techniques to detect and predict localised muscle fatigue," Sensors, vol. 11, no. 4, pp. 3545-3594, 2011.

O. Cakir, M. Engin, E. Z. Engin, and U. Yumrukaya, "Investigation of Muscle Fatigue by Processing EMG Signal," in Biomedical Engineering Meeting, 2009. BIYOMUT 2009. 14th National, 2009, pp. 1-3: IEEE.

J. Semmlow, "Biosignal and biomedical image processing: MATLAB-based applications. 2004," DOI, vol. 10, p. 9780203024058, 2004.

G. Ozmen, Y. Ozbay, and A. H. Ekmekci, "The evaluation of the muscle fatigue using frequency features in the cervical region," in Signal Processing and Communications Applications Conference (SIU), 2014 22nd, 2014, pp. 2078-2081: IEEE.

M. K. K. A. S. Alper and D. M. S. ÖZER, "Darbeli Doppler Laminar Kan Akış Sinyal Simülasyonuna STFT ve AR Spektral Analizlerinin Uygulanması."

M. Vetterli and J. Kovačević, "Wavelets and subband coding," 2007.

İ. Türkoğlu, "Durağan olmayan işaretler için zaman-frekans entropilerine dayalı akıllı örüntü tanıma, Fırat Üniversitesi," Fen Bilimleri Enstitüsü, Doktora Tezi, 2002.

N. ARI, Ş.Özer and Ö.H. Çolak, Dalgacık Teorisi (Wavelet). Palme Yayıncılık, 2008.

Y. Koçyiğit and M. Korürek, "EMG işaretlerini dalgacık dönüşümü ve bulanık mantık sınıflayıcı kullanarak sınıflama," İTÜDERGİSİ/d, vol. 4, no. 3, 2010.

E. D. Übeyli and I. n. Güler, "Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform," Computers in Biology and Medicine, vol. 34, no. 4, pp. 345-354, 2004.

J. G. Proakis, D. G. Manolakis, Ö. Salor, and A. Karamancıoğlu, Sayısal sinyal işleme: İlkeler, algoritmalar ve uygulamalar. Nobel Yayın Dağıtım, 2010.

Y. Özbay, R. Ceylan, and B. Karlik, "A fuzzy clustering neural network architecture for classification of ECG arrhythmias," Computers in Biology and Medicine, vol. 36, no. 4, pp. 376-388, 2006.

K. Polat, S. Kara, F. Latifoğlu, and S. Güneş, "Pattern detection of atherosclerosis from carotid artery Doppler signals using fuzzy weighted pre-processing and least square support vector machine (LSSVM)," Annals of biomedical engineering, vol. 35, no. 5, pp. 724-732, 2007.

Downloads

Published

12.12.2017

How to Cite

Ozmen, G., & Ekmekci, A. H. (2017). Classification of Cervical Disc Herniation Disease using Muscle Fatigue Based Surface EMG Signals by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 256–262. https://doi.org/10.18201/ijisae.2017533901

Issue

Section

Research Article