surface emg details

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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18. Signal Acquisition
Factors Affecting sEMG acquisition
 Tissue Characteristics
 Physiological Cross talk
 Changes in muscle geometry
 Electrode selection
 Electrode Placement
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19. Signal Acquisition
Sources of Noise affecting sEMG
 Power hum
 Inherent instability of the signal
 Motion artifacts
 Ambient noise
ECG artifacts
 Electrode dependent noise
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20. Signal Acquisition
Acquisition Circuit
Sensor Highpass Lowpass
ADC
Input filter filter
Driver
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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