Trends in Bioelectric signal
analysis
By
Dr. Ajat Shatru Arora
Principal, DAVIET, Jalandhar
Professor, EIE, SLIET, Longowal
Biomedical Engineering
Description
 “Biomedical engineering is a discipline
concerned with the development and
manufacture of prostheses, medical devices,
diagnostic devices, drugs,, and other therapies.
It is a field that combines the expertise of
engineering with medical needs for the
progress of health care. It is more concerned
with biological, safety, and regulatory issues
than other forms of engineering. It may be
defined as "The application of engineering
principles and techniques to the medical field.””
-.Wikipedia.org
Challenges in Man-machine
Interface
 Ethical and human subject protection
(externally applied energy interacting with
living tissue)
 Low rage measurement as compared to
non-medical parameters
 Many crucial parameter are inaccessible
(cardiac output etc.)
 Inherent variability ( most parameters vary
with time even under similar conditions)
 Harsh environment (Corrosive chemicals in
body)
 High risk of micro shock
Major Segments
Biomedical engineering can be segmented in two
major fields
 – physiological
 and industrial automation.
The physiological field concentrates more on
measuring, simulating, and analyzing
bioelectrical signals as well as modeling body
parts and processes. The industrial automation
field focuses on the automation of labs and
production lines along with the design and
testing of medical devices.
Sub-disciplines
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Bioinstrumentation
Biomaterials
Biomechanics
Biomedical computing & signal processing
Cellular, Tissue, and Genetic Engineering
Clinical Engineering
Medical Imaging
Orthopaedic Bioengineering
Rehabilitation Engineering
Biometrics
MEMS
Minimally invasive surgery
Bioinstrumentation
 The application of electronics and
measurement principles to develop devices
used in diagnosis and treatment of disease.
 A medical device is intended for use in:
 the diagnosis of disease or other conditions, or
 in the cure, mitigation, treatment, or
prevention of disease,
 EXAMPLES are the electrocardiogram, cardiac
pacemaker, blood pressure measurement,
hemoglobin oxygen saturation, kidney dialysis,
and ventilators
Biomaterials
 Describes both living tissue and
materials used for implantation.
 Choose appropriate material
 Nontoxic, chemically inert, stable,
and mechanically strong enough to
withstand the repeated forces of a
lifetime.
 Metal alloys, ceramics, polymers, and
composites
Biomechanics
 Mechanics applied to biological or
medical problems
 Study of motion, material deformation,
flow within the body and in devices, and
transport of chemicals across biological
and synthetic media and membranes.
 EXAMPLES: artificial heart and
replacement heart valves, the artificial
kidney
Biomedical computing & signal
processing
 Computers are becoming increasingly
important in medical signal
processing, from the microprocessor
used to do a variety of small tasks in
a single-purpose instrument to the
extensive computing power needed to
process the large amount of
information in a medical imaging
system
Biomolecular engineering
 Design molecules to achieve specific
biological function
 New drugs or therapeutic strategies
for treating disease.
 Cell biology, genetics, human
physiology, chemistry
 EXAMPLES: targeted drug delivery;
directed evolution of inhibitors of viral
growth
Micro-electromechanical
systems (MEMS)
 Microtechology and micro scale
phenomena is an emerging area of
research in biomedical engineering
 Many of life's fundamental processes
take place on the micro scale
 We can engineer systems at the cellular
scale to provide new tools for the study
of biological processes and
miniaturization of many devices,
instruments and processes
Minimally invasive medicine &
surgery
 Uses technology to reduce the debilitating
nature of some medical treatments.
 Minimally invasive surgery using
advanced imaging techniques that
precisely locate and diagnose problems
 Virtual reality systems that immerse
clinicians directly into the procedure
reduce the invasiveness of surgical
interventions
Medical imaging
 Medical/Biomedical Imaging is a major
segment of Medical Devices. This area deals
with enabling clinicians to directly or indirectly
"view" things not visible in plain sight (such as
due to their size, and/or location). This can
involve utilizing ultrasound, magnetism, UV,
other radiology, and other means.
Medical imaging
Imaging technologies are often essential to
medical diagnosis, and are typically the most
complex equipment found in a hospital
including:
 Magnetic resonance imaging (MRI)
 Projection Radiography such as X-rays and CT
scans
 Tomography
 Ultrasound
 Electron Microscopy
Medical Imaging
Computers are applied in medical imaging to:
 construct an image from measurements.
 identify
quantitative
parameters
of
clinical
interest
such
as
certain
distances, densities, etc
 improve
image
quality
by
image
processing,
compensate
for
imperfections in the image-generating
system, and reduce noise
Medical Imaging
 store and retrieve images
 reduce the amount of storage required
and the transmission time via image
compression techniques
 indirectly improve patient cares
Implants
 An implant is a kind of medical device made to
replace and act as a missing biological
structure (as compared with a transplant,
which indicates transplanted biomedical
tissue). The surface of implants that contact
the body might be made of a biomedical
material such as titanium, silicone or apatite
depending on what is the most functional. In
some cases implants contain electronics e.g.
artificial pacemaker and cochlear implants.
Some implants are bioactive, such as
subcutaneous drug delivery devices in the form
of implantable pills or drug-eluting stents.
Bioelectric Signals
Bioelectrical signal measurements from the
heart (electrocardiogram/ECG);
muscles (electromyograph/EMG);
skin (Galvanic skin response/GSR);
scalp (electroencephalograph/EEG);
eyes (electrooculogram/EOG
These bioelectrical signals are typically very small in amplitude and
require amplification to accurately record, display and analyze
the signals. Depending on the hardware and software used, the
biological amplifier serves not only to amplify the signal but
also to apply a range of filtering options for the removal of
unwanted signal artifacts.
Importance of Biosignals
 Diagnosis
 Patient monitoring
 Biomedical research
Characteristics of
Biosignals
 Often hidden in a background of
other signals and noise components.
 Generated by highly complex and
dynamic biological processes with
parameters usually more than a few
and varying continuously
Issues in biosignal acquisition
 Signal Conditioning
Amplification,
Isolation,
Filtering
 Sampling
Selection of sampling rate
 Selection of Software and Hardware
Signal Conditioning
Amplification
 Amplification
is
the
set
of
techniques used to boost a signal's
strength to better match the
analog-to-digital converter (ADC)
range
 Increases
the
measurement
resolution and sensitivity.
 Improves the signal-to-noise ratio.
Isolation
 Isolated signal conditioning devices pass the signal
from its source to the measurement device without
a physical connection.
 Benefits of isolation include:
a). Protection for expensive equipment, the user,
and data from transient voltages
b). Improved noise immunity
c).Ground loop removal
d).Increased common-mode voltage rejection
Isolation Techniques
Inductive Coupling
Optical Coupling
Capacitive Coupling
Multiplexing
Multiplexing is Transmission of multiple signals over a
single medium
Filtering
 Filtering is the process to reject unwanted
noise within a certain frequency range.
 All data acquisition applications are
subject to some level of 50 or 60 Hz noise
picked up from power lines or machinery.
 Most signal conditioners include the filters
specifically designed to provide maximum
rejection of 50 to 60 Hz noise.
Nyquist Sampling Theorem
 To reconstruct an analog signal waveform
without error from sample point taken at
equal
time
intervals,
the
sampling
frequency (Fs) must be greater than or
equal to twice the highest frequency(Fm)
component in the analog signal or
bandwidth or B.
Fs ≥ 2Fm or B
 Nyquist Rate
Sampling of Analog Signal
Sampled Analog Signal
When Fs ≥ 2Fm
DAQ Hardware
 DAQ hardware acts as the interface
between the computer and the outside
world.
 It digitizes incoming analog signals so that
the computer can interpret them
 DAQ hardware includes
Analog I/O, Digital I/O
Counters/Timers
Multifunctional:- combination of analog,
digital, and counter operations on a single
device.
Driver Software
 Basic driver software allows us to:
a). Bring data on to and get data off of the
board.
b). Control the rate at which data is acquired.
c). Integrate the DAQ hardware with computer
resources such as processor interrupts,
DMA and memory.
d). Integrate the DAQ hardware with signal
conditioning hardware.
e). Access multiple subsystems on a given
DAQ.
f). Access multiple DAQ boards
Biosignal Processing
 In order to derive the required information
from the bio signals:
-Disturbance should be filtered out
-The amount of data should be reduced by
discriminating only the most significant
ones related with the required information
Stages of Biosignal
Processing
 Signal acquisition
 Transformation and reduction of the
signals
 Computation of signal parameters that
are diagnostically significant
 Interpretation or classification of the
signals
Stages of Biosignal
Processing
Signal transformation
 Noise component:
 due to the electronics in the measuring
device,
 artifacts
related
to
the
patient’s
movements, or
 other
background
signals
recorded
simultaneously
 More data than actually needed to derive
parameters offering semantic information
Stages of Biosignal
Processing
Parameter selection
 Usually, relevant information is not
the direct result of a sample or
recording of a signal.
 Parameters bearing resemblance to
the signs and symptoms that are
used
to
make
diagnosis
are
extracted from the signal.
Stages of Biosignal
Processing
Signal classification
 the interpretation stage
 derived
relevant
human
decision
decision
features of selected
parameters used for
or
computer-assisted
making by means of
support methods
Application Areas of
Biosignal Analysis
 in ICUs
 integrating signals from multiple sources
 presenting information in the most
appropriate form
 interpreting variations over prolonged
time periods
 learning and recognizing profiles
 triggering “intelligent” alarms
Application Areas of
Biosignal Analysis
 Biosignals offer parameters that
support
medical
decision
making and trend analysis.
 Bio signal analysis techniques
help
to
extract
these
parameters accurately, analyze
and interpret them objectively.
Biomedical Instrumentation
Biomedical instrumentation contributes in
following ways
 Accurate measurement
 Long Term monitoring
 Understanding, Diagnosis and
management of disease
 Research
Biometrics
 Automated methods of verifying
the identity of a person based on
physiological behavioral
characteristics
Types of Biometrics
Biometric System
Salient Features of
Biometrics
 Biometric
makes
use
of
those
characteristics, which are universal, that
is, found in each and every human being.
For instance, fingerprints, voice, face print
and so on.
 Distinct body odours, handwriting skills and
other attributes are being included in
biometrics
analysis,
as
these
characteristics don’t change with growing
age of individuals.
Salient Features of
Biometrics
 The characteristics involved in biometrics
analysis can’t be stolen or copied. So, you
can’t expect anyone to steal your face or
eye vessels to use them for illegitimate
access.
 Interestingly, even if someone is able to
replicate your fingerprints and use it for
biometrics analysis, these systems can
instantly differentiate between a human
body and a plastic cast, on the basis of
body heat, temperature, blood flow and so
on.
Applications of Biometrics
 Biometric systems can be used as physical
access granting systems. The biometric
identifier serves as the key to open doors
to buildings and vehicles or to gain access
to computers and other devices.
 Secondly, biometric systems can be used
to establish entitlement to services and
rights that are restricted to a certain group
of individuals. In this case, the service or
right in question is only provided or granted
to individuals that are identified as
Applications of Biometrics
belonging to the group of recipients and rights
holders. Examples include social services
(prevention of welfare fraud), the right to
vote (voter registration), right of abode and
work (immigration), and all kinds of private
membership services or contractual rights.
 Biometric systems can be used for the
recording and association of facts. Such
uses
include
employee
attendance
monitoring, surveillance of public places,
forensics, archiving and retrieving personal
information such as health records.
Applications of EMG
Bio-Electric Signal
Processing Lab
48
Applications of EMG in
Ergonomics
► ANALYSIS OF DESIGN.
► RISK PREVENTION.
► ERGONOMIC DESIGN.
► PRODUCT CERTIFICATION.
Bio-Electric Signal
Processing Lab
49
Applications of EMG in
Ergonomics
Bio-Electric Signal
Processing Lab
50
Applications of EMG in
Ergonomics
Bio-Electric Signal
Processing Lab
51
Applications of EMG in Medical
Research
►EMG helps to
improve the
medical
research
studies by
detecting
activity levels in
muscles and
quickly
identifying
muscle
dysfunction.
Bio-Electric Signal
Processing Lab
52
Applications of EMG in Medical
Research
►FUNCTIONAL NEUROLOGY
►GAIT AND POSTURE ANALYSIS
►PROSTHETIC DEVICES
►ORTHOPEDICS
►SURGERY
Bio-Electric Signal
Processing Lab
53
Applications of EMG in Medical
Research
(FUNCTIONAL NEUROLOGY)
Bio-Electric Signal
Processing Lab
54
Applications of EMG in
Medical Research
(GAIT AND POSTURE ANALYSIS)
Bio-Electric Signal
Processing Lab
55
EMG For A Robotic Hand
 Figure shows the
highly integrated
approach to to use
EMG recording of
the human lower
arm in order to
control the opening
and closing of
three fingers of the
hand.
Bio-Electric Signal
Processing Lab
56
EMG Signal To Grasp
Objects
 The EMG interface
can be well used to
grasp objects.
 Since no force
feedback is
possible using this
interface, the
patient can use her
visual feedback to
interact with the
object via the
prosthetic hand.
Bio-Electric Signal
Processing Lab
57
Applications of EMG in
Medical Research
(PROSTHETIC DEVICES)
Bio-Electric Signal
Processing Lab
58
EMG For Repetitive Strain
Injury
 Electromyography
(EMG) is commonly
used for investigating
musculoskeletal
disorders
 To study muscle
activation at the motor
unit level through
multi-channel EMG in
order to develop
diagnosis and training
methods for muscle
activation
impairments.
Bio-Electric Signal
Processing Lab
59
EMG Biofeedback :
Treatment Of Tension
Headache
 Tension headache
is generally
described as a
bilateral dull ache,
pressure or caplike pain that is
usually located in
the forehead, neck
and shoulder
regions.
Bio-Electric Signal
Processing Lab
60
Applications of EMG in Sports
Science
(BIOMECHANICS)
► Biomechanics is
the scientific
study of forces
and the effects of
those forces on
and within the
human body.
Bio-Electric Signal
Processing Lab
61
Applications of EMG in Sports
Science
(MOVEMENT ANALYSIS)
► Monitor how
muscles are
utilized during
movement.
Bio-Electric Signal
Processing Lab
62
Micro/Nano applied to BME
Balloon Angioplasty
and
Stent Procedure
Micro/Nano applied to BME
Stent Procedure
Balloon Angioplasty
http://www.med.umich.edu/1libr/aha/aha_dil
ation_art.htm
http://www.mdmercy.com/vascular/discoveri
es/balloon_stent_gif_big.html
Examples
Automation in Biomedical
SLIET, Longowal
Electroencephalography (EEG)
Interpretation and Automated
Anesthesia Delivery
 Aspect Medical Systems (Natick, MA) has
developed monitors to assess the depth of the
anesthesia state based on the statistically
derived Bispectral Index (BIS) reflecting the
level of sedation. Community Hospitals
Indianapolis is successfully employing the BIS
monitor to improve the administration of
anesthesia during surgery and has found that
this technology contributes to improved patient
care and reduced costs. The Automated BIS
Controller, in development at the University of
Pittsburgh Medical Center, controls the rate of
anesthetic drug infusion using the BIS as a
feedback control.
Fuzzy Support Vector Machine for EMG
Pattern Recognition and Myoelectrical
Prosthesis Control
 For the optional control to the trans-femoral
prosthesis and natural gait, an ongoing investigation
of lower limb prosthesis model with myoelectrical
control was presented. In this research, the surface
electromyographic signals of lower limb were
extracted to be switch signal, and translate into
movement information. Considering every muscle’s
different physiologic tendency, fuzzy support vector
regression method was applied to establish an
intelligent black box that can interpret the
physiological signals to accurate information of knee
joint angle. It achieves a comparable or better
performance than other methods, and provides a
more native gait to the prosthesis user.
SLIET, Longowal
QUESTIONS ?
Thanks