A Machine Learning Technique to Analyze Surface EMG Signals in Normal and Diabetic Subjects

  • Anjaneya L H Visvesvaraya Technological University
  • Mallikarjun S. Holi Bapuji Institute of Engineering & Technology, Dept. of Biomedical Engineering, India.
  • Chandrashekar S J.J.M.Medical College, Dept. of Emergency Medicine, India
Keywords: surface electromyography, wavelet, artificial neural network, classification


The diabetes mellitus results in many musculoskeletal complications and detection of these problems is important in treatment, prevention of disability and improving the quality of life. Many attempts have been made to effectively acquire and analyze the surface electromyogram (sEMG) signals to understand the musculoskeletal problems in diabetes. The development of methodologies to extract the effective features from sEMG still remains a primary challenge. Previous studies have demonstrated that the sEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time-series analysis and the time-frequency domain methods, we propose the wavelet based method to extract the effective features of sEMG signals. The sEMG signals were first acquired and analyzed by the wavelet transform and the features were obtained. Then, these features were used as the input vectors to artificial neural network (ANN) classifier to discriminate diabetic or non-diabetic subject. The results show that significance of proposed feature extraction method with classification of diabetic and non-diabetic subjects with an accuracy of 97.06%.This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of sEMG signals and is suitable for classifying the normal subjects from diabetic subjects. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.

Author Biography

Anjaneya L H, Visvesvaraya Technological University

Biomedical Engineering

Associate Professor



(1) Castilho, L.V.; Lopes, H.S.; Tacla, C.A., "Modeling and Building an Ontology for Neuropediatric Physiotherapy Domain", Eighth International Conference on Hybrid Intelligent Systems, HIS ‘08, vol., no., pp.210-215, 10-12 Sept. 2008

(2) American Diabetes Association. “Diagnosis and Classification of Diabetes Mellitus”, Diabetes Care 36.Suppl 1 (2013): S67–S74. PMC.Web. 25 Sept. 2015.

(3) International Diabetes Federation, Diabetes Atlas, 3rd ed. Brussels, Belgium: International Diabetes Federation, 2007.

(4) Barakat, N.; Bradley, A.P.; Barakat, M.N.H., "Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus", IEEE Transactions on Information Technology in Biomedicine, vol. 14, no.4, pp. 1114-1120, July 2010

(5) M. Z. Al-Faiz, Yousif. I. Al-Mashhadany, “Human Arm Movements Recognition Based on EMG Signal”, MASAUM Journal Of Basic and Applied Sciences (MJBAS), vol. 1, issue 2, pp. 164-171, September 2009.

(6) Sacco ICN, Amadio AC. “Influence of the diabetic neuropathy on the behavior of electromyographic and sensorial responses in treadmill gait”, Clinical Biomechanics 2003; 18426–434, 2003.

(7) Van Schie, CH, Rawat F, Boulton, AJ. “Reduction of plantar pressure using a prototype pressure-relieving dressing”, Diabetes Care, 28(9):2236-7, 2005.

(8) Lawall H, Diehm C., “Diabetic foot syndrome from the perspective of angiology and diabetology”, 38(12):1149-59, 2009.

(9) Greene DA, Sima AA, Stevens MJ, Feldman EL, Lattimer SA, “Complications: neuropathy, pathogenetic considerations”, Diabetes Care, 15(12):1902-25, 1992.

(10) Katoulis EC, Ebdon-Parry M, Lanshammar H, Vileikyte L, Kulkarni J, Boulton AJ, “Gait abnormalities in diabetic neuropathy”, 20(12):1904-7, 1997.

(11) Sawacha Z, Gabriella G, Cristoferi G, Guiotto A, Avogaro A, Cobelli C., “Diabetic gait and posture abnormalities: a biomechanical investigation through three dimensional gait analysis”, Clin Biomech, 24(9):722-8, 2009.

(12) Akashi PM, Sacco IC, Watari R, Hennig E., “The effect of diabetic neuropathy and previous foot ulceration in EMG and ground reaction forces during gait”, Clinical Biomechanics; 23, pp. 584–592, 2008.

(13) Sawacha Z, Spolaor F, Guarneri G, “Abnormal muscle activation during gait in diabetes patients with and without neuropathy”, Gait Posture, 35(1): pp. 101–105, 2012.

(14) Motka, R.; Parmarl, V.; Kumar, B.; Verma, A.R., "Diabetes mellitus forecast using different data mining techniques," 4th International Conference on Computer and Communication Technology (ICCCT), ISBN: 978-1-4799-1571-2, pp. 99-103, 20-22 Sept. 2013.

(15) B. C. Callaghan, J. Hur, and E. L. Feldman, “Diabetic neuropathy: one disease or two?” Current Opinion in Neurology, vol. 25, pp. 536–541, 2012.

(16) J. C. Arezzo, “Clinical features and treatments of diabetic neuropathy. Quantitative sensory testing,” in Textbook of Diabetic Neuropathy, A. F. Gries, N. E. Cameron, P. A. Low, and D. Ziegler, Eds., Thieme Publishing Group, Stuttgart, Germany, pp. 184–189, 2003.

(17) Mohan, V.; Pradeepa, R., "Telemedicine in Diabetes Care: In rural India, a new prevention project seeks to fill in the screening gap.," in Pulse, IEEE , vol.5, no.3, pp.22-25, May-June 2014.

(18) Kumari, Sonu; Singh, Archana, "A data mining approach for the diagnosis of diabetes mellitus," 7th International Conference on Intelligent Systems and Control (ISCO), pp.373-375, 4-5 Jan. 2013.

(19) K. Mahaphonchaikul, D. Sueaseenak, C. Pintavirooj, M. Sangworasil and S. Tungjitkusolmun, "EMG signal feature extraction based on wavelet transform," International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), Chiang Mai, pp. 327-331, 2010.

(20) G. Wang, Z. Wang, W. Chen, and J. Zhuang, “Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion,” Medical and Biological Engineering and Computing, vol. 44, no. 10, pp. 865–872, 2006.