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Design and Implementation of a Variable Gain Amplifier for Biomedical Signal Acquisition

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In India and in other developing countries many poor people dies due to non-availability of proper health monitoring and health caring systems in hospitals. This is because of the high cost of available health monitoring and caring systems which cannot be afforded by small hospitals or organizations which provide free treatment for the poor people. The basic element in the health monitoring system is amplifier which amplify the biomedical signal to the appropriate level so that it can be detected faithfully by the further signal processing and display system. The aim of this paper is to design and implement a variable gain amplifier for biomedical signal. Variable gain provide the facility to increase or decrease the gain depending upon the acquiring signal and same amplifier hardware can be used for acquiring various biomedical signals. The circuit was simulated on Multisim and the prototype version was built on general PCB.
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Volume 2, Issue 2, February 2012 ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
Design and Implementation of a Variable Gain Amplifier for
Biomedical Signal Acquisition
Sachin Sharma1, Gaurav Kumar2, Dipak kumar Mishra3, Debasis Mohapatra4
M.Tech (2nd), Dept. of Instrumentation & Control Engineering, NIT Jalandhar
Jalandhar, Punjab(144011), India
AbstractIn India and in other developing countries many poor people dies due to non-availability of proper health monitoring and
health caring systems in hospitals. This is because of the high cost of available health monitoring and caring systems which cannot be
afforded by small hospitals or organizations which provide free treatment for the poor people. The basic element in the health
monitoring system is amplifier which amplify the biomedical signal to the appropriate level so that it can be detected faithfully by the
further signal processing and display system. The aim of this paper is to design and implement a variable gain amplifier for biomedical
signal. Variable gain provide the facility to increase or decrease the gain depending upon the acquiring signal and same amplifier
hardware can be used for acquiring various biomedical signals. The circuit was simulated on Multisim and the prototype version was
built on general PCB.
Keywords-Multisim; Biomedical Signals; Gain; Filters; TinaTI;
I. INTRODUCTION
Around the globe, especially in India and in other
developing countries, there are a large number of poor people
who died every year due to poor health monitoring facilities in
the hospitals. This amount is very high in the hospitals which
are located in the villages where people are very poor and
hospitals don’t have enough fund to procure the costly
machines which are used only for health monitoring. In urban
areas too there are big hospitals which have all type of facilities
but the cost of treatment there is a big issue for a poor person.
Many poor people spend their whole life saving money just on
treatment in the hospitals. The situation is worse for the new
born babies. Every year countless new born babies die due to
non-availability of basic infrastructure to hold the babies. So, if
we can decrease the cost of the equipments being used by the
hospitals then they will have enough funds to provide the
infrastructure for poor patients.
Being an engineer our work is to lower down the cost by
implementing some innovating idea in the present available
solutions. This thing inspired us to develop a low cost
monitoring system and the same monitoring system can be
used to acquire and display various biomedical signals like
ECG, EMG etc, just by varying the gain of the amplifier used
to amplify the signal. Amplifier acts as the heart of the
monitoring system, as it amplifies the biomedical signals which
have very low voltage level. Amplifier amplifies the signal
upto that level where it can be faithfully detected by the further
processing system to process and display.
In this paper we have designed and implemented an
amplifier for biomedical application having adjustable gain.
The gain is tunable manually according to the application. We
have used AD620 IC for designing instrumentation amplifier,
TL084 for implementing high pass and low pass filters on the
hardware and IC UAF42 for implementing Notch filter. The
filter and amplifier is also simulated on the Multisim 2008 and
TinaTI software. The hardware of the amplifier is tested on
labview using the National Instruments data acquisition card
NI ELVIS.
II. NATURE OF BIOMEDICAL SIGNAL
Living organisms are made up of many component systems
the human body includes the nervous system, the
cardiovascular system, and the musculoskeletal system, among
others. Each system is made up of several subsystems that
carry on many physiological processes. Physiological
processes are complex phenomena, including nervous or
hormonal stimulation and control; inputs and outputs that could
be in the form of physical material, neurotransmitters, or
information; and action that could be mechanical, electrical or
biochemical. Most physiological processes are accompanied by
or manifest themselves as signals that reflect their nature and
activities. Such signals could be of many types, including
biochemical in the form of hormones and neurotransmitters,
electrical in the form of potential or current, and physical in the
form of pressure of temperature.
Diseases or defects in a biological system cause alterations
in its normal physiological processes, leading to pathological
processes that affect the performance, health, and general well-
being of the system. A pathological process is typically
associated with signals that are different in some respects from
the corresponding normal signals that are different in some
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Page | 194
respects from the corresponding signals. If we possess a good
understanding of a system of interest, it becomes possible to
observe the corresponding signals and assess the state of the
system, i.e. by observing the signal related to different
physiological process we can find out whether there is a
problem in the system or not.
The origins of these biomedical signals related to the
physiological process are from action potentials. The action
potential (AP) [1-4] is the electrical signal that accompanies the
mechanical contraction of a single cell when stimulated by an
electrical current. It is caused by the flow of sodium,
potassium, chloride and other ions across the cell membrane.
The action potential is the basic component of all bioelectrical
signals. It provides information on the nature of physiological
activity at the single-cell level.
A cell in its resting state is said to be polarized. Most cells
maintain a resting potential of the order of -60 to -100 mV until
some disturbance or stimulus upsets the equilibrium. When a
cell is excited by ionic currents or an external stimulus, the
membrane changes its characteristics and begins to allow the
flow of sodium and potassium ions through it. A new state of
equilibrium is reached after the rush ions stops. This change
represents the beginning of the action potential, with a peak
value of about +20mV for most cells. An excited cell
displaying an action potential is said to be depolarized; the
process is called depolarization.
Nerve and muscle cells repolarize rapidly, with and action
potential duration of about 1 ms. Heart muscle cells repolarize
slowly, with an action potential duration of 150-300 ms. The
action potential is always the same for a given cell, regardless
of the method of excitation or the intensity of the stimulus
beyond a threshold. After an action potential, there is a period
during which a cell cannot respond to any new stimulus,
known as the absolute refractory period (about 1 ms in nerve
cells). Figure 1 shows action potentials recorded from cells. An
action potential propagates along a muscle fiber or an
unmyelinated nerve fiber.
Fig. 1: Diagram for action potential
There are various types of biomedical signals related to
different processes. Some are given as: ENG: The
electroneurogram is an electrical signal observed as a stimulus
and the associated nerve action potential propagate over the
length of a nerve. It may be used to measure the velocity of
propagation of a stimulus or action potential in a nerve[ ].
EMG: the electromyogram signal is from the motor neuron of
the central nervous spinal cord. ECG: the electrocardiogram is
the electrical manifestation of the contractile activity of the
heart, and can be recorded fairly easily with surface electrodes
on the limbs or chest. The ECG is perhaps the most commonly
known, recognized and used biomedical signal. The rhythm of
the heart in terms of beats per minute may be easily estimated
by counting the readily identifiable waves. EEG: the
electroencephalogram represents the electrical activity of the
brain. This signal is used to diagnose the various problems
related to the brain injury. EGG: the electrical activity of the
stomach consists of rhythmic waves of depolarization and
repolarization of its constituent smooth muscle cells. The
activity originates in the mid-corpus of the stomach, with
intervals of about 20s in humans. Frequency and voltage ranges
of these signals are shown in table 1.
Table 1: Frequency and voltage ranges of various Biomedical signals
S.No.
Name of the Signals
Frequency
voltage
1.
EMG
10-350 Hz
< 50µV
2.
ECG
.05-100 Hz
1-10mV
3.
EEG
.5-30Hz
< 1mV
4.
EGG
.2-10MHz
-80- -30 mV
III. EXTRACTION OF THE BIO SIGNAL
There are different methods and locations according to the
signal which is to be taken. e.g. If we want to extract ECG
signal there are three different type of configuration of the
leads. This signal can be taken from chest, or right arm, left
arm and right or left leg. There are 3 lead system and 12 lead
system too for acquiring the signal.
Other example is if we want to take EEG signal it will be
taken from the scalp. It has 24 lead system or 32 or 128 lead
system. These leads can be attached to the surface of the scalp
or there are needle electrodes which can penetrate in upper
layer of the scalp.
A. Electrodes for signal extraction
Electrodes [5-7] are the transducers which converts the
ionic current in the body to the electronic current. The
Biosignals can be measured by applying conductive elements
or electrodes to the skin surface, or invasively within the
muscle. Surface extraction of the signal is the more common
method of measurement, since it is non-invasive and can be
conducted by personnel other than medical doctors, with
minimal risk to the subject [8].
Two types of surface electrodes are commonly in use. The
first one is dry electrodes in direct contact with the skin and the
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Page | 195
second one is jelled electrodes using an electrolytic jell as a
chemical interface between the metallic parts of the electrode
[8].
Jelled electrodes uses an electrolytic jell as a chemical
interface between the skin and the metallic part of the
electrode. Oxidative or reductive chemical reaction take place
in the contact region of the metal surface and the jell. Silver-
silver-chloride (Ag-AgCl) is the most common composite for
the metallic part of gelled electrodes. The AgCl layer allows
current from the muscle to pass more freely across the junction
between the electrolyte and the electrode. This introduces less
electrical noise into the measurement, as compared with
equivalent metallic electrodes (e.g. Ag). Due to this fact, Ag-
AgCl electrodes are used in over 80% of surface application
[9].
Jelled electrodes can either be disposable or reusable.
Disposable electrodes are the most common since they are very
light. Disposable electrodes come in a wide assortment of
shapes and sizes, and the materials comprising the patch and
the form of the conductive jell varies between manufacturers.
With proper application, disposable electrodes minimize the
risk of electrode displacement even during rapid movements.
In our experiment for the acquisition purpose of the EMG
signal we have used disposal jelled electrodes. Figure 2 shows
the picture of the electrodes used in our experiment.
Fig.2: Disposal Jelled Electrodes
IV. AMPLIFIER DESIGN
Surface amplifier picks up and detects the desired
biosignals with the use of electrodes, and adds another
electrode between the two electrodes in order to reduce the
noise and improve the common-mode rejection ratio. Reduce
the impact of EMG "common mode" component through the
two pickup electrodes, and achieve the amplification of EMG
acquisition through the amplification "differential Mode"
section.
Fig.3: Differential mode Electrode Arrangement
The electrode placement on the body is in itself requiring a
good knowledge of the origination of various signals. E.g. for
ECG recording we have to place the electrode at different
section of the body [12]. The Fig.3 shows the electrode
arrangement for the EMG signal measurement.
Now, First of all, pick up the desired signal power with
two electrodes and pre-zoom the bio signal by use of low-
noise differential pre-amplification of Instrumentation
Amplifier AD620, and put the reference level signal into
AD620. While put the signal which preamp from AD620 into
the high-pass filter circuit to weed out low-frequency noise
and DC component. Second, weed out the impact of 50Hz
frequency noise in the system by use of the 50Hz frequency
notch filter. Now put the bio signal into the low-pass filter
circuit to remove the high-frequency noise. The last, put the
transformation of the bio signal into the follow-up of the A/D
conversion unit circuit in order to further processing the
signal. The following block diagram shows the all steps
involved into amplifier design.
Fig.4: Conditioning circuit for the Amplifier
A. PreAmplifier
The preamplifier makes use of the Instrumentation
amplifier AD620 [10]. This chip has a lot of benefits such as
low cost, high accuracy and requires only one external resistor
to set gains of 1 to 10,000. Absolute value trimming allows the
Surface
Biomedical
Signal
Acquisition
Signal
Preamplification
using Instrumentation
amplifier AD620
Notch
Filter
Low Pass
Filter
Analog to
Digital
Conversion
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Page | 196
user to program gain accurately (to 0.15% at G=100) with
only one resistor. It has 100 dB min common-mode rejection
ratio, Low noise and excellent ac specifications over 120 kHz
bandwidth. It can effectively reduce the electrode contact with
the skin produces noise.
The internal gain resistors of AD620 are trimmed to an
absolute value of 24.7kΩ, allowing the gain to be programmed
accurately with a single external resistor. Then, The gain
equation is given by
𝐺=49.4𝑘𝛺
𝑅𝑔
+ 1
So, we can vary the gain by varying the resistor Rg. We
have put the variable resistor instead of constant value in order
to make it more versatile for different signal acquisition.
Hence by use of same amplifier we can acquire any
biomedical signal just by adjusting its gain for the particular
Biomedical signal.
B. High Pass Filter
High pass filter is used to remove the motion artifacts and
D.C. component in the signal. Keeping in mind the frequency
range of measuring biomedical signals we have provided the
variable resistance in order to change the cutoff frequency of
the high pass filter. We have implemented the 1st order active
filter, in unity gain mode, using the IC TL84 [10]. This IC has
four operational amplifiers. We have used only one OP-AMP
to implement the filter [11].
The cut-off frequency of the high pass filter is given by the
following relation:
𝑓=1
2𝜋𝑅𝐶 𝐻𝑧
We can vary the value of resistance R to vary the cutoff
frequency of the filter. The following figure (Fig.5) shows the
simulation results of the high pass filter. Here the cut off
frequency or -3dB frequency is 0.78 Hz.
Fig.5: Simulation of High pass filter
C. Nothch Filter
After preamplifier, we must use the 50Hz notch filter to cut
off 50Hz noise. It is usually to use a low-pass filter and a high-
pass filter. The notch frequency is given by:
𝑓=1
2𝜋𝑅𝐶 𝐻𝑧
𝑄=1
2(2 − 𝐴𝑢𝑝 ); 𝐴𝑢𝑝 = 1 + 𝑅𝑓
𝑅1
However, when the accuracy of this notch is bad, the notch
will affect the notch frequency and quality factor. So, use the
burr-brown UAF42 chip to compose the T notch. Set
parameters and make the Notch center frequency to be 49.8Hz
and 50.2Hz by use of software CAD-FILTER42 from burr-
brown.
D. Low Pass Filter
After 50Hz notch filter, it will use low-pass filter to cut off
high frequency noise affected. Most of biomedical signal are
restricted to the 500Hz frequency range. We have
implemented a low pass filter having the cut off frequency or
-3dB frequency 400Hz. The cut-off frequency of the low pass
filter is given by the same formula as in the case of high pass
filter.
The following figure (Fig. 6) shows the simulation results
of the low pass filter.
C1
1.0uF
V1
1 V
60 Hz
0Deg
2
R1
150k
0
U1A
TL084CD
3
2
11
4
1
1
3
V2
12 V
V3
12 V
5
6
0
V2
12 V
V3
12 V
U1B
TL084CD
3
2
11
4
1
4
C2
1.0uF
R2
390
7
V4
1 V
60 Hz
0Deg
8
9
0
5
6
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Page | 197
Fig. 6: Simulation of Low pass filter
V. RESULTS
The Analog to digital conversion section is used in order to
see the waveforms on computer. In our prototype in place of
A/D conversion we have used National Instruments data
acquisition device NI ELVIS. Figure 7-9 shows the row
waveform of ECG and EMG signals taken from the amplifier.
Fig. 7: Setup of the Instruments
Fig. 8: Row ECG signal on labview
Fig. 9: Row EMG signal
VI. CONCLUSION
Biomedical signals are so weak that they can be easily
impacted by the surrounding noise. So, more care is to be
taken for electrodes and lead wires, as they take the signal to
the amplifier. To design the biomedical signal acquisition
device it is not only to amplify the biomedical signal, but more
work is accurately extracted the biomedical signal from a
strong noise environments. Amplifier must weed out the 50Hz
frequency out of the signal and shield the surrounding noise.
The further analysis by using digital filters can be done after
taken the signal to the computer. But this article is by use of
the hardware design and software analysis composing; present
the acquisition of biomedical signal interference amplification
device which can effectively remove the noise.
REFERENCES
[1] David Prutchi, Michael Norris, “Design and Development of Medical
Electronic Instrumentation”, Wiley publications.
[2] “Introduction to Biomedical Engineering”, 2nd edition,Elsevier
Academic Press, by John D. Enderle, Susan M.Blanchard, Joseph D.
Bronzino.
[3] “ Text Book of Medical Physiology”, 11th edition, Elsevier Saunders, by
Arthur C. Guyton and John E. Hall.
[4] “Wiley Encyclopedia of Biomedical Engineerig”, edited by Metin Akay.
[5] “Sensors, Nanoscience, Biomedical Engineering and Instruments”, The
Electrical Engineering Handbook, 3rd edition, by Richard C. Dorf.
[6] Biomedical Instrumentation & Measurement by Carr & Brown”, 2nd
edition, Pearson publication.
[7] “Handbook of Biomedical Intrumentation”, 3rd edition, by Khandpur.
[8] Scott Day, Important Factors in Surface EMG Measurement, Boretech
BiomedicalLtd,Manual.[Online]Available:http://www.bortec.ca/Images/
pdf/EMG%20measurement%20and%20recording.pdf.
[9] Gary L. Solderberg, Ph.D., PT, “Recording Technieqes”, Chapter 3,
NIOSH Selected Topicsin Surface EMG Document 91-100-c Electrode
Chapter[online]Available:http://www.humanicses.com/SelectedTopicsE
MGsNIOSH.pdf.
[10] Datasheet of IC AD620 Instrumentation amplifier & Quad Op-amp IC
TL084.
[11] Prakash Biswagar, “OPAMP APPLICATIONS”, E&C Dept. RV
College of Engineering, Bangalore. [online] Available:
http://forum.vtu.ac.in/`edusat/elec&cir/ur/session_10.doc.[online].
[12] J. vaisanen, J. Hyttinen, M. Puurtinen, P. Kauppinen, J. Malmivuo,
Prediction of Implantable ECG lead system by using thorax models”,
preceedings of the 26th annual Intenational Conference of the IEEE
EMBS, San Francisco, CA, USA, September 1-5,2004, p.p. 809-812.
Authors
Sachin Sharma was born at Dadri, Uttar Pradesh, India on 5 July,
1987. Currently, He is pursuing his M.tech degree in Instrumentation
and Control Engineering from NIT Jalandhar. He
did his B.tech in Electronics and Communication
Engineering from GLA Institute of Technology &
Management, Mathura. He has published several
papers in International conferences and
International journals on robotics and autonomous
systems. His area of research interest includes
robotics, signal processing, neural networks,
embedded systems, system designing, biomedical
application and artificial intelligence.
Gaurav Kumar was born at Jalalpur village, Aligarh, U.P., India on
20 May, 1989. He is pursuing his M.tech
degree in Instrumentation and Control
Engineering from NIT Jalandhar. He did his
B.tech in Electronics and Instrumentation
Engineering from Hindustan College of
Science and Technology, Mathura. He has
published many papers in International
Journals and Conferences. His area of
interest includes control and automation.
Volume 2, issue 2, February 2012 www.ijarcsse.com
Page | 198
Dipak kumar mishra was born at jajpur, odisha, India on 1st july
1984. Currentlys he is pursuing his M.Tech
degree in Instrumentation and Control
Engineering. He did his B.tech in Applied
Electronics and Instrumentation from SIT
Bhubaneswar. He has published several
papers on biomedical in various
international conferences. His area of
research interest includes Biomedical signal
processing and systems.
Debasis Mohapatra was born at Bhubaneswar, India on 10th May
1986. Currently he is pursuing his M.tech degree
in Instrumentation & Control Engineering from
NIT Jalandhar. He did his B.tech in Applied
Electronics and Instrumentation from SIT
Bhubaneswar. He has published several papers
on process control. His area of research interest
includes Process control and biometrics.
... Combining Eqs. (12), (19) and (20), the output current is given by ...
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The Electromyogram signals or EMG signals are generated whenever there is any muscular activity. These signals can be detected easily over the surface of the body. The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional neuromuscular electrical stimulation. Muscle signal (EMG) based switching systems may be an option for people who retain some ability to contract certain muscles but may not be able to operate a mechanical switch. This may be because the particular muscle that can be contracted is not suitable for operating a switch or because the muscle contraction is not strong enough to operate the switch. A proper positioning of the recording electrodes in relation to the stimulation electrodes, and a proper processing of the recorded signals is required to reduce the stimulus artifact and the non-voluntary contribution. As these signals are generated whenever there is any muscular activity, so, we can recognize any muscular activity made by the human by measuring the amplitude of the signal. Based on decision made upon the amplitude of the EMG signal we can take many decisions or we can make machine of our interest. In this paper we have made a real time machine which can classify the muscle activity between two actions: relaxed position and contracted position. The prototype hardware has been tested and verified for the working in real time.
Article
Introduction The Electromyography (EMG) signal, also referred to as the myoelectric signal (MES), acquired from the forearm skin surface provides valuable information about neuromuscular activities. Electromyography (EMG) signals have the properties of non-stationary, nonlinear, complexity, and large variation. These lead to difficulty in analyzing EMG signals. To make a system based on the EMG first we need to extract the features of the acquired EMG signal based on which it can be further classified for various hand movements. This technique is also called as pattern classification. Wavelet transform (WT) has emerged as an effective tool to extract the useful information from EMG and as well as various other biomedical signals [1]. There is a vast collection of literature which has focused on the evaluation and investigation of an optimal feature extraction obtained from wavelet coefficients [2-6]. Most of these research works have paid more attention to identifying hand motion commands. Hence, in this paper we have investigated the same for some hand movements from one useful forearm muscle as a representative EMG signal. WT is a time-frequency analysis method that is successful in the analysis of non-stationary signals including the EMG signal. The main advantage of wavelet transform is that the selection of dimensions of feature vector is very flexible and it provides approximately same results for various dimensions of feature vector. In our previous work [7-8] towards active prosthesis machines for disabled persons, we have made the real time machine which can classify two hand motions effectively. In this work, as the further development in the real time prosthesis machines, our aim is to classify more than two hand motions. In this paper, we used two popular and successful EMG features in both clinical and engineering applications, root mean square (RMS) and mean absolute value (MAV) [9], as the representative features. Introduction to Wavelet Analysis In a basic course of signal processing [10], we assume that the signals last forever. For example, while calculating the Fourier transform, we represent any signal in terms of basis functions and these basis functions last from t = −1 to t = +1, where t denotes time. However, no signal in this world can last forever. Thus, we should deal with signals in finite domains. In the basic course of signal and systems we deal with Fourier transforms and they deal with sine waves. Sine waves have many nice properties. They occur naturally, most analytic and smoothest possible periodic functions. Moreover they can form a very good basis for representing other waveforms. Addition, differentiation, subtraction or Integration of sine wave gives sine wave itself. Any linear combination of all these operations on a sine wave results in a sine wave of the same frequency. But the biggest drawback of sine waves is that they need to last forever. If the sine wave is truncated (a one sided sine wave, for example), the response to this signal by a system, in general, is different from the response which would be obtained if the signal would be a sine wave from t = −1 to t = +1. There would be transients which are not periodic. All the properties mentioned above, are no longer valid. So, to be realistic in our demand we deal with wavelets. Wavelets are waves that last for a finite time, or more appropriately, they are waves that are not predominant forever. They may be significant in a certain region of time and insignificant elsewhere or they might exist only for finite time duration. For example, a sine wave that exists only between t = 0 and t = 1 msec is, in principle, a wavelet (though not a very good one), a wave that doesn't last forever. Wavelet analysis was developed from Fourier analysis which was performed in short time period [11]. The size of window can be modified by using the real time frequency analysis. Consequently, the signal analysis can be performed at Elixir Control Engg. 50 (2012) 10320-10324
Article
Full-text available
Important Factors in Surface EMG Measurement is a customer support paper that was requested by several researchers using the Bortec Octopus EMG measurment system. The technical specifications for the Octopus were developed by Dr. Day with systems design and fabrication conducted by electronics engineer Boris Kacmar to meet the need for a full EMG bandwidth system that did not chop out significant power from the EMG signal, as was/is the problem with other systems on the market. The Octopus met the needs of 1) ease of use, 2) reliability, 3) full signal measurement and 4) affordability. The support paper is helpful for any student or researcher that wants a concise overview of important factors in recording the surface EMG during static or dynamic muscle activity, regardless the brand of recording system. It has proven to be a very popular article, in part, because it was not designed to push product sales: unlike this abstract :)
Conference Paper
Full-text available
New implantable ECG devices may provide more stable and noiseless measurements compared to body surface ECG measurements. When the electrodes are moved to inside of the body the way the ECG measurement is done is changing. Modeling can be an effective way to study effects of implantation to the capacity of electrodes to measure ECG compared to surface measurements. This paper introduces a project where effects of electrode implantation to the magnitude and direction of lead sensitivity to detect cardiac source, lead field, was studied with a model of the thorax as a volume conductor. The study was based on 3D Finite Difference Method (FDM) featuring Visible Human Man. The results of the study indicate that the effect of electrode implantation under the skin (5-15 mm) to the way they measure ECG is rather small. Magnitude change is dependent of the studied lead and the change of the sensitivity to heart's equivalent sources in direction of lead field is minor.
Book
Under the direction of John Enderle, Susan Blanchard and Joe Bronzino, leaders in the field have contributed chapters on the most relevant subjects for biomedical engineering students. These chapters coincide with courses offered in all biomedical engineering programs so that it can be used at different levels for a variety of courses of this evolving field. Introduction to Biomedical Engineering, Second Edition provides a historical perspective of the major developments in the biomedical field. Also contained within are the fundamental principles underlying biomedical engineering design, analysis, and modeling procedures. The numerous examples, drill problems and exercises are used to reinforce concepts and develop problem-solving skills making this book an invaluable tool for all biomedical students and engineers. New to this edition: Computational Biology, Medical Imaging, Genomics and Bioinformatics.
Recording Technieqes NIOSH Selected Topicsin Surface EMG Document 91-100-c Electrode Chapter
  • L Gary
  • Ph D Solderberg
  • Pt
Gary L. Solderberg, Ph.D., PT, " Recording Technieqes ", Chapter 3, NIOSH Selected Topicsin Surface EMG Document 91-100-c Electrode Chapter[online]Available:http://www.humanicses.com/SelectedTopicsE MGsNIOSH.pdf.
Design and Development of Medical Electronic Instrumentation
  • David Prutchi
  • Michael Norris
David Prutchi, Michael Norris, "Design and Development of Medical Electronic Instrumentation", Wiley publications.
NIOSH Selected Topicsin Surface EMG Document 91-100-c Electrode Chapter
  • L Gary
  • Ph D Solderberg
  • Pt
Gary L. Solderberg, Ph.D., PT, "Recording Technieqes", Chapter 3, NIOSH Selected Topicsin Surface EMG Document 91-100-c Electrode Chapter[online]Available:http://www.humanicses.com/SelectedTopicsE MGsNIOSH.pdf.