EMG signals are generated by the muscle in the human body. It is used in the medical field for diagnosis purposes. The objective of the this project is design a system that is able to differentiate the EMG signals coming from different patients. From the input signals, the system generates the power spectrum, determine signal parameters and use as input to a rule based classifier to identify the respective patient.
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscle.
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: How to perform EMG instrument Figure 1 shows how to perform EMG instrument by placing the electrode at the muscle.. The input EMG signal can be captured nicely and useful for diagnosis if the placing of electrode heavily considered
Figure 2: Time & frequency graphs for different place of muscles The figure 2 below, show the different signals captured for different place of muscles.
METHODOLOGY Figure 3 : The block diagram of analysis of EMG signal.
Based on figure 1, this is a general block diagram of this experiment.
The system will receive two difference EMG signals coming from two different patients. Then, the system will identify the signal belong to which patient.
FLOW TO DESIGN THE SYSTEM Figure 4: The step involves for designing the complete system.
The first step is we need to identify the EMG characteristic in terms of its power and frequency. Those characteristic help us to continue the next step.
From the input signals, the system generates the power spectrum. By using the fast Fourier transformation the EMG power spectrum can be obtained with a better resolution. We examined the power spectrum of the EMG patents define the parameters that can be used to identify the various patients. The signal parameters then would be used as input to a rule base classifier to be implemented in the software.
Figure 5. The flow chart of Matlab Programming. The analysis and classification of EMG signal to differentiate the signal coming from which patient can be verified.
RESULT Figure 6: Input EMG signal in time domain
Figure 7 Power Spectrum
Figure 8 : To Display Result
Figure 7: Input EMG signal in time domain
Figure 8 Power Spectrum
Figure 9: To Display Result
Figure 10: Verify result on GUI for patient 1
Figure 11: Verify result on GUI for patient 2
The power spectrum that was generated from the input signal was examined in order to identify the suitable signals parameters to differentiate the signal from respective patients.
In terms of power spectrum, the obvious characteristics of both signals which are in terms of amplitude, power spectrum density can be easier analyzed and classified.
Several parameters can be accounted to use as the input of rule base classifier which were median frequency, mean frequency, the amplitude in terms of root mean square, maximum and minimum power spectrum density.
Simply using only one of the above signal parameter, the system was able to differentiate the EMG signals coming from which patient.
The figures 6 showed the results obtained when the system was loaded the EMG signal from patient 1. The displaying box was used to verify the performance of our system.
While the figures 7 were the results obtained when the system was loaded the EMG signal coming from another patient which was patient 2.
The study investigates the rule of 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.
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