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1. Analysis of Surface Electromyography
Parameters
GUIDED BY
Internal Guide: Ms. Latha
Assistant Professor (Sr. G), Department of Electronics and Communication Engineering,
Amrita School of Engineering, Bangalore
External Guide: Dr. A. S .Aravind
Professor and Head, Department of Biomedical Engineering,
Institute of Aerospace Medicine
Sreenivasan Meyyappan BLENU4ECE08098
Swathi Sivakumar BLENU4ECE08102
Varun Praveen BLENU4ECE08110
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2. The human body is an engineering marvel
Biomedical research has lead to generation of enormous amount
of information
Engineers bring problem solving and quantitative skills to
biomedical research
Medical and engineering are diverse but Interdependent fields
Engineering marvels like pacemaker, heart lung machine,
dialysis machines etc
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3. Only a few bio - signals have been analyzed
Electromyography (EMG) is an experimental technique
concerned with the development, recording and analysis of
myoelectric signals
Francesco Redi experimented with Electric Eel
Galvani found direct relation between muscle contraction and
electricity
Clinical use of Surface Electromyogram (sEMG) began only in
1960’s
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4. Two types of EMG
sEMG has applications in sports training, treatment
planning, performance enhancement etc.
Shift of focus from manual to machine based analysis
Our focus is to provide a quantitative solution to clinical
sEMG analysis
Hardware design for analysis of signal
Software code as an aid for parameter extraction
Standardization by SENIAM
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5. Phase 1
Literature Review and understanding the hardware design aspects and signal
processing
Phase 2
Design of hardware and understanding the bio-correlations
Phase 3
Signal Processing
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7. Rodney A Rhoades, George A Tanner, “Medical
Physiology”
Peter Konrad, “ The ABC of EMG : A Practical
Introduction to Kinesiological
Electromyography”, Version 1.0 April 2005
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8. For correct electrode placements on the muscle
body
To differentiate between Myopathic and
Neuropathic disorders
Understanding the bio- correlations
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9. Stuart Ira Fox, “Human Physiology”, 11th
edition
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10. Stuart Ira Fox, “Human
Physiology”, 11th edition
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12. Stochastic
Superimposition of multiple Motor Unit Action
Potentials
Amplitude- 0-500µV
Bandwidth- 0-4kHz
Usable range- 10-500Hz
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13. Peter Konrad, “ The ABC of EMG : A Practical Introduction to
Kinesiological Electromyography”, Version 1.0 April 2005
Dr. Roberto Merletti, Politecnico Di Torino, Italy, 1999
“Standards for Reporting EMG data”, International Society
of Electrophysiology and Kinesiology, 1999
Bjorn Gerdle, Stefan Karlsson, Scott Day and Mats
Djupsjobacka, “Acquisition, Processing and Analysis of
Surface Electromyogram”, Chap. 26
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14. To extract parameters of clinical importance from the sEMG
Parameters analysed in time and frequency domain
Time domain analysis
Full wave rectification:
absolute value of the signal samples
removes negative spikes
Parameters extracted
▪ Maximum peak
Maximum potential attained by muscle
▪ Mean Rectified Value
Average of the rectified signal
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15. Zero crossings
gives the extent of muscle activity
gives the number of Action Potentials generated
based on Intermediate Mean Value Theorem(IMVT)
Integrated sEMG
gives the overall performance of the muscle
based on peak amplitude and Interpolation
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16. Gianluca De Luca, “ Fundamental Concepts in
EMG Signal Acquisition”, Delsys, Revised 2.1,
March 2003
Bjorn Gerdle, Stefan Karlsson, Scott Day and
Mats Djupsjobacka, “Acquisition, Processing and
Analysis of Surface Electromyogram”, Chap. 26
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17. Signal Acquisition
Aspects to be considered
Factors affecting sEMG acquisition
Sources of noise affecting sEMG
Pre-acquisition procedures
Acquisition Circuit
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21. Signal Acquisition
Sensor Input
This stage consists of
Electrodes (sensing element)
Instrumentation amplifier
Electrodes
Differential inputs are taken from
Active Electrode
Reference Electrode
Picks up electric potentials at skin surface
Converts ionic current to electrical voltage
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22. Signal Acquisition
Instrumentation Amplifier
Amplifies differential input from electrodes
Removes common mode noise
High pass Filter
Cut off frequency = 10Hz
Gain = 10V/V
Removes low frequency motion artifacts
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23. Signal Acquisition
Low pass filter
Cut off frequency = 500Hz
Gain = 100V/V
Removes out electrode and equipment noise
Notch filters are avoided – loss of usable signal components
ADC
Digitizing the analog sEMG input
High bit resolution to depict more levels (16 bit)
Sampling frequency > Nyquist frequency (>1000Hz)
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24. Signal Acquisition
Driver Circuit
Remove common mode noise
Provide a proper baseline for the signal
Prevent high frequency electrical signal from entering the subjects body
Consists of
Low pass filter (fc = 8kHz)
Ground electrode
Ground electrode features
Fairly larger than active and reference
Placed at electrically neutral sites
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25. Hardware Front
Choice of components
Bread board implementation
PCB construction
Software Front
Implementation of pre-conditioning techniques (SENIAM approved)
Attempting sound analysis of sEMG
Implementation and extraction of frequency domain analysis and
remaining time domain parameters
Validation on test subjects
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26. Timeline Module of Work to be Completed
1st March 2012 – 23rd March 2012 Hardware design and construction
26th March, 2012 – 13th April, 2012 Completion of software design and
parameter extraction
16th April, 2012 – 30th April, 2012 Validation on test subject and
completion of report
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27. Gianluca De Luca, “ Fundamental Concepts in EMG Signal Acquisition”,
Delsys, Revised 2.1, March 2003
M.B.I Reaz, M.S. Hussain and F.Mohd-Yasin, “Techniques of EMG signal
analysis: Detection, Processing, Classification and Applications” Biol.
Proced. Online 2006;8(1):11-35, March 23, 2006
Dr. Scott Day, “Important factors in Surface EMG Measurement”, Bortec
Biomedical Ltd.
Gary D Klasser, DMD; Jeffrey P Okeson, DMD, “The clinical usefulness of
surface electromyography in the diagnosis and treatment of
temporomandibular disorders”, American Dental Association, 2005
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