DIGITAL SIGNAL PROCESSING IN BIOMEDICAL ENGINEERING
ABSTRACT :-
Digital Signal Processing, a field which has its root
in the 17th & 18th century mathematics has become an important modern tool
in a multitude diverse field of science & technology. The techniques &
applications of this field are as old as Newton & Gauss & as new as
digital computers & integrated circuits.
digital Signal Processing means processing signals in
digital domain, which includes
# Modifying signal characteristics
# Multiplying two signals ( Modulation, Correlation)
# Filtering
# Averaging
DSP can extract one signal from another. The purpose of
such processing may be to estimate characteristic parameter of a signal into a
form which is in some sense more desirable. DSP can analyze ECG or EEG to
extract some characteristic parameter.
Application of Signal
processing in biomedical:
Signal processing in general has a rich history &
its important is evident in such diverse fields as
biomedical engineering.
# Echo cancellation
# Noise cancellation
# Spectrum Analysis
# Detection
# Correlation
# Filters
# Computer Graphics
# Image Processing
# Data Compression
# Machine vision
# Sonar
# Array Processing
# Guidance
# Robotics etc.
Signals:
Signals play an important role in our daily life.
Examples of signals that we encounter frequently
are speech, music, picture & video signals.
A signal is a function of independent variable such as
time, distance, position, temperature, pressure.
E.g.
1. A black & white picture is a representation of
light intensity as a function of two
spatial coordinator.
2. The video signal consists of a sequence of images,
called frames and is a function
of three
variables :two spatial coordinators and time.
Most signals we encounter are generated by natural
means. However a signal; can also be generated synthetically or by a computer
simulation.
A signal carries information.
The objective of the signal processing is to roughly
speaking concerned with the mathematical representation of the signal and the
algorithmic operation carried out on it to extract the information present.The
representation of signal can be in terms of basis function in the domain of the
original independent variable or it can be in terms of basis functions in a
transformed domain. Likewise the information extraction process may be carried
out in the original domain of the signal or in a transformed domain.
Characterization and
classification of signals:
Depending on the nature of the independent variables
and the values of the function defining the signal, various types of signal can
be defined.
Variable ---- 1.Continuous
Function
of Independent variable
2.Discrete
Real Valued
Function of
Independent variable
Complex
valued
-
A signal can be generated by a single source
or is called Scalar signal.
-
A signal can be generated by multi source or
is called Vector signal (multi channel signal.)
Classification:
Dimension: 1-D, 2-D, 3-D< Multi dimensional
Amplitude, Waveform, Continuous Time, Discrete time
Analog,
Digital, Sampled Data, Quantized box case
Deterministic, random.
Typical Signal
Processing Operation:
Various types of signal processing operations are
employed in practice. In case of analog signals, most signal processing
operations are usually carried out in the time domain.
In the case of discrete time signals, both time domain
and frequency domain operations are employed. In either case the desired
operations are implemented by a combination of some elementary operation. These
are also usually implemented in real time or near time, even though in certain
application they may be implemented off time.
Elementary Time Domain
Operations:
-
Scaling, Amplitude- attenuation
-
Integration
-
Differentiation
-
Delay- advance
-
Addition
-
Product
Filtering:
One of
the most widely used complex signals processing operation is filtering.
Filtering is used to pass certain frequency components in a sign through the
system without any distortion and to block other frequency components. The
system implementing this operation is called as filter.
Pass band: The range of frequency that is blocked by
the filter is called as stop band.
Various types of filters can be defined depending upon
the nature of filtering operation
-
Low pass- High pass
-
Band pass- Band reject
-
Multi pass- Comb filter
-
Notch filter
-
Interference- Noise removal
Generation of Complex
Signals:
- Real valued- Real signals
- Complex valued –Complex Signals
- All naturally generated signals are real
valued signals
- In some applications it is desirable
characteristics. A complex signal from
real
signal having more desirable characteristics. A complex signal can be
generated from a real signal by employing a Hilbert Transformer.
Modulation and
Demodulation
Multiplexing and Demultiplexing
Signal Generation
An
equally important part of signal processing is synthetic signal generation.
Examples of Typical
Signal in Biomedical engineering:
We will now examine a couple of examples of some
typical biomedical signals and their subsequent processing in typical
application.
Electroencephalogram
(EEG)
The summation of the electrical activity caused by the
random firing of individual neutrons in the brain is represented by the EEG
signal. In multiple EEG recordings, electrodes are placed at various position
on the scalp with two common electrodes placed on the earlobes, and the
potential difference between the various electrodes are recorded. An example of
multiple EEG trace is shown in Fig(a)
Both frequency domain and time-domain analysis of the
EFG signal have been used for the diagnosis of epilepsy, sleep disorders,
psychiatric malfunctions, etc. To this end the EFG spectrum is subdivided into
the following five bands.
Range Band
# Delta 0.5 to
4 Hz
# Theta 4 to 8 Hz
# Alpha 8 to 13
Hz
# Beta 13 to
22 Hz
# Gamma 22 to 30 Hz
The delta wave is normal in the EFG signals of the
children and the sleeping adults. Since it is not common in alert adult, its
presence indicates certain brain disease. The theta wave is usually found even
though it has been observed in alert adult. The alpha wave is common in all
normal humans and is more pronounced in a relaxed and awake subject with closed
eyes. Likewise, the beta activity is common in normal adults. The EFG exhibits
rapid, low voltage waves, called rapid- eye- movement (REM) waves, in a subject
dreaming during sleep. Otherwise, in a sleeping subject, the EFG contains
bursts of alpha like waves, called sleep spindles. The EFG of epileptic patient
exhibits various types of abnormalities, depending on the type of epilepsy that
is caused by uncontrolled neural discharges.
Electrocardiogram ( ECG
) Signal :
The electrical activity of the heart is represented by
the ECG signal. A typical ECG signal; trace is shown in figure (b). The ECG
trace is essentially a periodic waveform. One such period of ECG waveform is
depicted in figure (c) represents one cycle of the blood transfer process from
the heart to the arteries. This part of the waveform is generated by an
electrical impulse originating at the sinoatrial node in the atrium of the
heart. The impulse causes contraction of the atria, forcing the blood in each
atrium to squeeze into its corresponding ventricle. The resulting signal is
called P wave. The atrioventricular node delays the excitation impulse until
the blood transfer from atria to ventricle is completed. The excitation impulse
then causes contraction of the ventricle, squeezing the blood into the arteries
and generating QRS part of the ECG waveform. During this phase the atria are
relaxed and failed with blood. The T-wave of the waveform represents the
relaxation of the ventricles. The complete process is repeated periodically
generating ECG trace.
Each portion of ECG waveform carries various types of
information for the physician analyzing a patient’s heart condition. For
example, the amplitude and timing of the P and QRS portions indicate the
condition of the cardiac muscle mass. Loss of amplitude indicates muscle
damage, where as increase amplitude indicates abnormal heart rates. To long a
delay in the atrioventricular node is indicated by a very T-R interval.
Likewise, blockage of some or all of the contraction
impulses is reflected by intermittent synchronization between P- and QRS-
phase. Most of these abnormalities can be treated with various drugs and
observing the new ECG waveforms taken after the drug treatment can again
monitor the effectiveness of the drug.
In practice, there are various types of externally
produced artifacts that appear in the ECG signal. Unless these interference are
removed, it is difficult for a physician to make a correct diagnosis. A common
source of noise is the 50-Hz power line whose radiated electric and magnetic
field induction. Other sources of ECG instruments through capacitive coupling
and/or magnetic induction. Other sources of interference are the
electromayographic signals that are potentials developed by contracting
muscles. These and interference can be removed with careful shielding and
processing techniques.
Electromayogram ( EMG)
Heart Rate:
Blood pressure:
0-15mm of Hg (Venous)
Breathing rate:
Biomedical signals come in all shapes and sizes.
WHY DSP?
Digital signal processing of an analog signal consists
basically of three steps:
1. Conversion of the analog signals into a digital
form.
2. Processing of the digital version.
3. Conversion of the processed digital signal into an
analog form. Figure shows the overall scheme in a block diagram form.
Advantages:
1. Digital circuits are less sensitive to tolerance of
the component values and are fairly
independent
of temperature, aging and most other external parameters.
2. The digital circuits are small in volume quantity
and do not require any adjustments
either during
construction and later while in use.
3. Recent advances in VLSI ( Very large Scale
Integrated circuits ) , sophisticated
integration
of complex DSP systems on a single chip – Compactness.
4. Flexibility
5. Resource Sharing
6. Programmability
7. Simple implementation
8. Indefinite storage capability- Size &Time
9. Applicability.
Disadvantages:
1. Increased system complexity because of the need for
additional pre and post
processing
devices such as A/D and D/A converters and filters and complex digital
circuitery.
2. Limited range of frequency available for processing
as fs >= 2f
3. Resolution and word length of register effects on
the performance and specification of
the system.
4. Cost increases and the power consumption also.
However the advantages far outweigh disadvantages in
various applications and with the continuing decreases in the cost of digital
processor hardware, application of digital signal processing are increasing
rapidly.
References:
(1) Digital
signal processing
By Sanjit k. Mitra
(2) Digital
signal processing
By Oppenheim & Schafer
(3)
Biomedical digital signal processing
By Wiellis J.
Tompkins
(4) www.dspguide.com
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