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Dsp lab report- Analysis and classification of EMG signal using MATLAB.
Analysis and Classification of Electromyogram (EMG) SignalsNur Hasanah Binti Shafei, Nur Sabrina Binti Risman, Kartini Binti Ibrahim, Idayu Binti Mohamed Rasid Faculty of Electrical Engineering Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Darul Ta’zim Abstract – The early diagnosis in medical healthcare application were really needed and crucial. It is therefore important to devise accurate methods of diagnosis. Currently, methods of diagnosis include assessing the patients’ history, blood tests and muscle biopsies. The latter two methods, whilst being relatively accurate, may take weeks to obtain a result . This paper investigates another commonly used method is electromyography by analysis and classification the EMG signals. The system has successfully implemented by using MATLAB’s software that was able to differentiate the EMG signal coming from different patients. The signal Figure 1: How to perform EMG instrument from respective patients can be easily identified by development of Graphical User Interface (GUI). The input EMG signal can be captured nicely and useful for diagnosis if the placing of I. INTRODUCTION electrode heavily considered. The figure 2 below, show the different signals captured for different Electromyography (EMG) is a technique for place of muscles. evaluating and recording the electrical activity produced by skeletal muscles. EMG is performed using an instrument called an electromyograph, to produce a record called an electromyogram. An electromyograph detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated . The action of nerves and muscle is essentially electrical. Information is transmitted along nerves as a series of electrical discharges carrying information in pulse repetition frequency.  Figure 1 shows how to perform EMG instrument by placing Figure 2: Time & frequency graphs for different the electrode at the muscle. place of muscles
EMG signals acquire advanced methods then would be used as input to a rule base classifierfor detection, decomposition, processing, and to be implemented in the software.classification. The purpose to provide efficientand effective ways of understanding the signaland its nature, we further point up some of thesoftware implementation for identifying thesignals coming from different patients. Thisknowledge may help in medical healthcare centerto develop more powerful, flexible, and efficientapplications . II. METHODOLOGY First and foremost, a block diagram mustbe designed to be the basic reference. Based on Figure 4: The step involves for designing thefigure 3, this is a general block diagram of this complete system.experiment. The system was received twodifference EMG signals coming from two differentpatients. Then, the system was identified the signalbelong to which patient.Patient 1 Identify the SYSTEM Patient’sPatient 2 (MATLAB) signalFigure 3: The block diagram of analysis of EMGsignal. In order to design the system, there are sixsteps to be done. Below, in the figure 4, there werethe sequences of our procedure. The first step waswe needed to identify the EMG characteristic interms of its power and frequency. Thosecharacteristic helped us to continue the next step. From the input signals, the systemgenerated the power spectrum. By using the fastFourier transformation the EMG power spectrumcan be obtained with a better resolution. Weexamined the power spectrum of the EMG patentsto define the parameters that can be used to Figure 5: The flow chart of Matlab Programming.identify the various patients. The signal parameters
The coding implemented for the above ideas in elseif(absx > 10)figure 5 were as follow : % xH = avex2 msgbox(The signal belong to patient 2)a) For load the data end%DSP Laboratory % Parameter3: Root mean square%Analysis and Classification of EMG Signals rms_x = sqrt(mean(Y.^2))%Load Data from user absrms = abs (rms_x)A = load (emg1.txt); % A = load (emg2.txt) if % Classification for the result processemg2 signal xH = 0;Y = fft(A(:,1),1024); % FFT of sample data if (absrms < 370)figure, plot (A) % Show FFT Figure % xH = avex1;xlabel(Time (ms)) msgbox(The signal belong to patient 1)ylabel (Amplitude (uV)) elseif(absrms > 370)figure,stem (abs(Y)); % To generate power spectrum % xH = avex2xlabel(Frequency) msgbox(The signal belong to patient 2)ylabel (Amplitude) endb) There are several parameters that can be used % Parameter4: Maximum power as an input of rule base classifier. The system z = abs (Y); designed for obtaining results by simply using maxz = max (z) one of the following signal parameters. % Classification for the result process xH = 0;% Parameter1: Median value if (maxz < 5200)medianx = median (Y) % xH = avex1;absx = abs(median msgbox(The signal belong to patient 1)% Classification for the result process elseif(maxz > 5200)xH = 0; % xH = avex2if (absx < 580) msgbox(The signal belong to patient 2)% xH = avex1; endmsgbox(The signal belong to patient 1)elseif(absx > 580) % Parameter5: Minimum power% xH = avex2 z = abs (Y);msgbox(The signal belong to patient 2) minz = min (z)end % Classification for the result process xH = 0;% Parameter2: Average value if (minz < 20)avex = mean (Y) % xH = avex1;absx= abs(avex) msgbox(The signal belong to patient 1)% Classification for the result process elseif(minz > 20)xH = 0; % xH = avex2if (absx < 10) msgbox(The signal belong to patient 2)% xH = avex1; endmsgbox(The signal belong to patient 1)
Last but not least, the analysis andclassification of EMG signal to differentiate thesignal coming from which patient can be verified. III. RESULTS AND DISCUSSIONS Figure 7(a): The EMG signal from patient 2 in time domain Figure 6(a): The EMG signal from patient 1 in time domain Figure 7(b): The power spectrum of EMG signal from patient 2Figure 6(b): The power spectrum of EMG signal from patient 1 Figure 7(c): The result displayed to identify the signal coming from patient 2 The EMG signal is biomedical signal that is a collective electrical signal acquired from any muscle organ that represents a physical variable of interest. As we know this type of signal was Figure 6(c): The result displayed to identify the normally a function of time and described in terms signal coming from patient 1 of its amplitude, frequency and also phase. So, the
power spectrum that was generated from the input EMG signals coming from which patient. The figuressignal was examined in order to identify the 6 showed the results obtained when the systemsuitable signals parameters to differentiate the was loaded the EMG signal from patient 1. Thesignal from respective patients. In terms of power displaying box was used to verify the performancespectrum, the obvious characteristics of both of our system. While the figures 7 were the resultssignals which are in terms of amplitude, power obtained when the system was loaded the EMGspectrum density can be easily analyzed and signal coming from another patient which wasclassified. patient 2. Last but not least, figures 8 below show the verification of the results by using GUI. Several parameters can be accounted touse as the input of rule base classifier which were IV. CONCLUSIONSmedian frequency, mean frequency, the amplitudein terms of root mean square, maximum and The study investigates the rule basedminimum power spectrum density. classifier from the EMG signal parameters to differentiate the EMG signal coming from different patients. This application of EMG signals that were generated by the muscles in human body commonly use in medical field for diagnostic purpose. According to our experimental results, the suitable parameters were determined to successful implemented to complete system. The performance of the system which is the ability to identify the EMG signals coming from different patients was verified. V. REFERENCES Figure 8(a): the result obtaining for patient 1 by using GUI . Martin, L., Diagnosis of Neuromuscular disease using surface EMG with neural network analysis. COIN512(Comp.) Project Brief . David, M. Blake, Procedures Offered for Lexington Neurology General Services. Lexington, KY. . Malcown, C. Brown, The Medical Equipment Dictionary- Electromygram. 2007. Liverpool, United Kingdom. . M.B.I Raez, et al. Zhu, J., et al. Techniques of EMG Signal Analysis: Detection, Processing, Classification and Applications. 2006 Figure 8(b): the result obtaining for patient 2 by . Wan Mohd Bukhari Bin Wan Daud using GUI Classification of EOG signals of Eye Movement Potentials. 2009 Simply using only one of the above signalparameter, the system was able to differentiate the