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Amrita School of Engineering, Bangalore

Sreenivasan Meyyappan BLENU4ECE08098Swathi Sivakumar BLENU4ECE08102Varun Praveen BLENU4ECE08110

Analysis of Surface Electromyography Parameters

GUIDED BYInternal Guide: Ms. Latha

Assistant Professor (Sr. G), Department of Electronics and Communication Engineering,Amrita School of Engineering, Bangalore

External Guide: Dr. A. S .AravindProfessor and Head, Department of Biomedical Engineering,

Institute of Aerospace Medicine

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Motivation

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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Introduction

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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Our focus 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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Phases of project

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Literature Review

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Basic Muscle Physiology

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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Understanding Biology

For correct electrode placements on the

muscle body

To differentiate between Myopathic and

Neuropathic disorders

Understanding the bio- correlations

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Structure of Muscle

Stuart Ira Fox, “Human Physiology”, 11th edition

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Muscle Structure

Stuart Ira Fox, “Human Physiology”, 11th edition

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Sliding Filament Theory

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Characteristics of sEMG

Stochastic

Superimposition of multiple Motor Unit

Action Potentials

Amplitude- 0-500µV

Bandwidth- 0-4kHz

Usable range- 10-500Hz

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Signal Analysis

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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Analysis of sEMG 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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Analysis of sEMG

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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Signal Acqusition

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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Aspects to be considered

Factors affecting sEMG

acquisition

Sources of noise affecting

sEMG

Pre-acquisition procedures

Acquisition Circuit

Signal Acquisition

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Signal Acquisition

Factors Affecting sEMG

acquisition

Tissue Characteristics

Physiological Cross talk

Changes in muscle geometry

Electrode selection

Electrode Placement

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Sources of Noise affecting

sEMG

Power hum

Inherent instability of the signal

Motion artifacts

Ambient noise

ECG artifacts

Electrode dependent noise

Signal Acquisition

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Sensor Input

Highpass filter

Lowpass filter

ADC

Driver Circuit

Acquisition Circuit

Signal Acquisition

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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

Signal Acquisition

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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

Signal Acquisition

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Low pass filter

Cut off frequency = 500Hz

Gain = 100V/V

Removes out electrode and equipment noise

Notch filters are avoided – loss of usable signal

componentsADC

Digitizing the analog sEMG input

High bit resolution to depict more levels (16 bit)

Sampling frequency > Nyquist frequency

(>1000Hz)

`

Signal Acquisition

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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

Signal Acquisition

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The Road Ahead

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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Timeline

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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References

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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Thank You