Detection of muscles diseases based on EMG signal by using locally recurrent neural networks (LRNNs) techniques
Keywords:Locally Recurrent Neural Networks (LRNNS), Electromyography (EMG) Signals, Graphical User Interface (GUI), Levenberg-Marquardt Back Propagation (LMBP).
Muscle diseases occur in all age groups and can cause serious physical disability. The effect of such diseases is severe when children and young adults are affected. The needs of these patients are numerous and complicated and are frequently inadequately met. Some muscle diseases respond well to medical treatment, whereas many physical disabilities can be improved or prevented.
This paper presents the implement of The Locally Recurrent Neural Networks (LRNNs) to detect the muscles diseases based on real measurement of electromyography (EMG) signals. The LRNNs training achieved by using Levenberg-Marquardt back propagation (LMBP) to get high accuracy through the identification and recognition process. Many processing applied with EMG signal through LRNNs before recognition the diseases. Finally design the simulation of this work by using Graphical User Interface (GUI) through MATLAB. Satisfactory results are obtained with the case study of real implement on human arm muscles.
 A. K. G. Murphy, â€œEffective Information Display and Interface Design for Decomposition-based Quantitative Electromyographyâ€, M.Sc thesis, University of Waterloo, Canada, 2002.
 N. BU, â€œEMG-Based Motion Discrimination Using a Novel Recurrent Neural Networkâ€, Journal of Intelligent Information Systems, 21:2, 113â€“126, 2003. https://doi.org/10.1023/A:1024706431807.
 T. Tsuji1 â€œPattern classi cation of time-series EMG signals using neural networks", international journal of adaptive control and signal processing, 2000.
 M. M. Lowery, â€œA Multiple-Layer Finite-Element Model of the Surface EMG Signal", MAY 2002.
 O. Bida, â€œInfluence of Electromyogram (EMG) Amplitude Processing in EMG-Torque Estimationâ€, M.Sc Thesis, worcester polytechnic institute, Electrical Engineering, January 2005.
 M. B. I. Reaz, â€œTechniques of EMG signal analysis detection, processing, classification and applications", March 23, 2006.
 A. G. Murphy, " Effective Information Display and Interface esign for Decomposition-based Quantitative Electromyographyâ€, thesis, 2002.
 L. Mesin, D. Farina, â€œSimulation of Surface EMG Signals Generated by Muscle Tissues With Inhomogeneity Due to Fiber Pinnation", Sep. 2004.
 A. Hamilton, â€œPhysiologically Based Simulation of Clinical EMG Signals", FEBRUARY 2005.
 M. Z. Al-Faiz, Yousif. I. Al-Mashhadany, â€œHuman Arm Movements Recognition Based on EMG Signalâ€, MASAUM Journal of Basic and Applied Sciences (MJBAS) Volume 1 Issue 2, PP 164-171, (September 2009).
 Y. I. Al-Mashhadany, "Measurement of human leg joint angle through motion based on electromyography (EMG) signal", The Engineering Conference of Control, Computers and Mechatronics, ECCCM2011, university of Technology, 30-31, January, 2011.
 Y. Al Mashhadany, Nasrudin Abd Rahim, â€œReal-Time Controller for Foot-Drop Correction by Using SEMG Sensorâ€, Proc IMechE Part H: J Engineering in Medicine, 227(4) 373â€“383. Jan 2013.
 N. A. Shrirao, N. arender, P, Reddy, â€œNeural network committees for finger joint angle estimation from surface EMG signalsâ€, BioMedical Engineering OnLine 8:2, 2009. https://doi.org/10.1186/1475-925X-8-2.
 V. R. Mankar, A. A. Ghatol, â€œDesign of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removalâ€, Hindawi Publishing Corporation Advances in Artificial Neural Systems Volume 2009, Article ID 942697, 9 pages doi: 10.1155, 2009.
 O. A. Alsayegh, â€œEMG Based Human Machine Interface Systemâ€, IEEE Transaction of Biomedical Engineering, 0-7803-6535-4/00/ pp 925-928, 2000. https://doi.org/10.1109/ICME.2000.871510.
 M. M. Lowery, N. S. Taflove, â€œA Multiple Layer Finite-Element Model of the Surface EMG Signal â€ , IEEE Transaction of Biomedical Engineering, Vol. 49, no.5, PP 446-454, May 2002.
 L. Mesin, D. Farina, â€œ Simulation of Surface EMG Signals Generated by Muscle Tissues with Inhomogeneity Due to Fiber Pinnation â€, IEEE Transaction on Biomedical Engineering Vol. 51, no. 9, PP 1521-1529, September 2004.
 Motion Lab Systems, Inc, â€œA software user guide for EMG Graphing and EMG Analysis EMG Analysisâ€, Updated Thursday, February 26, 2009.
 Y. I. Al-Mashhadany, â€œDesign and Analysis of Virtual Human Arm Driven by EMG Signalâ€, BOOK, ISBN: 978-3-8433-7973-1, 2011, LAP LAMBERT Academic Publishing GmbH & Co. KG, 2011.
 EMGLAB software Version 0.9 Userâ€™s Guide, â€œThe MathWorks, at www.mathworks.com, May 2008.
 E. Farago, â€œDevelopment of an EMG-based Muscle Health Model for Elbow Trauma Patientsâ€œ, thesis 2018.
 A. Goen â€œClassification of EMG Signals for Assessment of Neuromuscular Disordersâ€œ, International Journal of Electronics and Electrical Engineering Vol. 2, No. 3, September, 2014.
 R. Bahadur, S. Rehman, â€œA Robust and Adaptive Algorithm for Real-time Muscle Activity Interval Detection using EMG Signals â€œ,11th International Joint Conference on Biomedical Engineering Systems and Technologies Volume 4: BIOSIGNALS, pages 89-96, ISBN: 978-989-758-279-0, 2018. https://doi.org/10.5220/0006536200890096.
 M. Khan, JK. Singh, M. Tiwari, â€œA Multi-Classifier Approach of EMG signal classification for Diagnosis of Neuromuscular Disordersâ€œ, J Bioengineer & Biomedical Sci S3:003. https://doi.org/10.4172/2155-9538.S3-003.
 F. Sadikoglu, C. Kavalcioglu, B. Dagman, â€œElectromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle diseaseâ€œ, 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, 24-25 August 2017, Budapest, Hungary.
 V. Kehri, R.N. Awale , â€œEMG Signal Analysis for Diagnosis of Muscular Dystrophy Using Wavelet Transform, SVM and ANNâ€œ,Vol. 11(3), p. 1583-1591, Biomedical & Pharmacology Journal, September 2018.
 A. Gupta, T. Sayed, R. Garg, R. Shreyam, â€œEMG Signal Analysis of Healthy Neuropathic Individualsâ€œ, Materials Science and Engineering 225 (2017) 012128.
 M. Wirth, S. Grad, D. Poimann, R. Richer, J. Ottman, â€œExploring the Feasibility of EMG Based Interaction for Assessing Cognitive Capacity in Virtual Realityâ€œ, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018.