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6.3. Bioinstrumentation design
The purpose of using bioinstrumentation is to monitor the output of a sensor or sensors and
to extract some useful information from signals that are produced by sensors.
Acquiring discrete-time signal and storing this signal in computer memory from a continuous-
time signal is accomplished with analog-to-digital (A/D) converter. After analog signals have
been processed which are based on analog filters such as low-pass or high-pass filters, A/D
converter uniformly samples the continuous-time waveform and transforms it into a sequence
of numbers, one every 
t
k
seconds. The A/D converter also transforms the continuous-time
waveform into a digital signal, which is converted into computer words and stored in
computer memory. To adequately capture the continuous-time signal, the sample frequency
has to be carefully selected to ensure any signal information is not lost. The minimum sampling
frequency is twice the highest frequency content of the signal based on the sampling theorem
from communication theory. In reality, we often adopt the sampling frequency from five to
ten times the highest frequency content of the signal so as to achieve better accuracy by
reducing aliasing error.
• Biological signal categories in human body
The electrical, chemical and mechanical activity that occurs during this biological event often
produces signals that could be detected and analyzed. Biological signals are the record of a
biological event such as a beating heart or a contracting muscle. Hence, biological signals
contain useful information which could reflect human’s activities and physiology, that’s to
say, biological signal could be used for biomedical diagnosis. Biological signals are classified
Advances in Bioengineering
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into bioelectric signals, biomagnetic signals, biochemical signals, biomechanical signals,
bioacoustic signals and biooptical signals.
Nerve and muscle cells generate bioelectric signals that are the result of electrochemical
changes within and between cells. When plenty of cells are stimulated, an electric field is then
generated that propagates through biological tissues. These changes in extracellular potential
may be measured on the surface of tissue or organism by using surface electrodes. The
electrocardiogram (ECG) is an example of this phenomenon. Different organs in body,
including the heart, brain, lungs, and liver, also generate weak magnetic fields that could be
detected with magnetic sensors. The strength of magnetic field is much weaker than the
corresponding physiological bioelectric signal. Magnetic sensors could be used to detect
biomagnetic signals. Magnetocardiography (MCG) is a specific example of such phenomenon.
Biochemical signals contain information about changes in the concentration of various
chemical agents in the body. The concentration of various ions such as calcium and potassium
in cell can be measured and recorded. Oxygen sensor is used to detect oxygen concentration
in body. Mechanical functions of biological systems, including motion, displacement, tension,
force, pressure and flow, also produce measurable biological signals. Blood pressure sensor is
a measurement of the force that blood exerts against the walls of blood vessels. Change in
blood pressure can be recorded as a waveform by blood pressure sensor. Bioacoustics’ signals
are a special subset of biochemical signals which involve vibrations. Many biological events
could produce acoustic noise. For example, the flow of blood through the valves in the heart
can be used to determine whether motion is operating properly. Besides these, the respiratory
system, joints and muscles could also produce bioacoustic signals that propagate through the
biological medium and can be often measured at the skin surface by acoustic sensors. Bioop‐
tical signals are generated by the optical or light induced attributes of biological systems.
Biooptical signals can occur or be introduced to measure a biological parameter with an
external light medium such as the measurement of health of a fetus by red and infrared light.
• Noise
Measurement signals are always corrupted by noise in the bioinstrumentation system.
Interference noise occurs when unwanted signals are introduced into systems by external
sources such as telephone magnetic wave, power line and transmitted radio. Interference noise
needs to be effectively reduced by careful attention to the circuit wiring configuration to
minimize coupling effect.
Interference noise is introduced by power lines, fluorescent lights, AM/FM radio broadcasts,
computer clock oscillator, laboratory equipment and cellphone. Electromagnetic energy
radiating from noise source is injected into the amplifier circuit or into the patient by capacitive
or inductive coupling. Even action potentials from nerve conduction in the patient generate
noise at the sensor/amplifier surface. Filters are also used to reduce the noise and to maximize
the signal-to-noise(S/N) rate at the input of the A/D converter.
Low frequency noise could be eliminated by high-pass filter with the cutoff frequency set above
the noise frequency. High frequency noise could be reduced by low-pass filter with the cutoff
frequency set below the noise frequency and above the frequency of biological signal which
Biomedical Sensor, Device and Measurement Systems
http://dx.doi.org/10.5772/59941
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is being monitored. Power line noise is a very difficult problem in biological monitoring
because the 50-or-60-Hz frequency is usually at the range of biological signal which could be
monitored. Band-stop filters are commonly used to reduce the power line noise. The notch
frequency in the band-stop filters is set to the power line frequency of 50 or 60Hz with the
cutoff frequency located a few Hertz to either side.
The second type of noise is called inherent noise. Inherent noise arises from random processes
that are fundamental to the operation of circuit’s elements and thus is reduced by a good circuit
design practice. While inherent noise is reduced, it can be never eliminated. Low-pass filters
are used to reduce high-frequency components. However, noise signals within the frequency
range of biological signals being amplified cannot be eliminated by this filtering approach.
• Computer
Computer is a main device which is used to display the biological signals being monitored.
However some low or high level languages such as machine language, FORTRAN, visual C+
+, MATLAB or LabView, have to be used to realize the operation on the acquisition data from
biological body. When computers are used to acquire physiological data, programming
instruction tell computer when acquisition data should begin, how often samples should be
taken from how many sensors, how long acquisition data should continue, and where the
digitized data should be stored. The rate at which a system acquires sample depends on the
speed of computer clock’s frequency and the number of computer instruction that could be
completed in order to realize a sample. Of course, some computers are utilized to control the
gain on the input amplifiers so that biological signals could be adjusted during data acquisition.
In other systems, the gain of data acquisition has to be adjusted.