CHAPTER 2
REVIEW OF
LITERATURE OF PC
BASED MEDICAL
INSTRUMENTATION
CHAPTER 2
REVIEW OF LITERATURE OF PC BASED MEDICAL INSTRUMENTATION
2.1
PC BASED MEDICAL INSTRUMENTATION AND LATEST
DEVELOPMENT:
Over the years, use of computers in our day to day lives has been expanding. We
use them at work, at home, at school, on our cell phones, to predict the weather, to get
information, and so on. Computers and the use of the Internet have infiltrated the world
like no other technology. The way in which the world works and stores information has
changed with the use of computer. The medical field is one that depends on computers as
much or more than other fields.
The progress of medicine in the past few years has been rapid and extremely
beneficial to humanity. Computers has played such a large role in this advancement, that
it is likely that they will become an absolute necessity in every part of medical practices
in the near future. Computer usage has changed medicine for the better. It has benefited
surgeons in record-keeping in a way that no other advancement has.
Many of the modern-day medical equipment have small, programmed computers.
Many of the medical appliances of today work on pre-programmed instructions. The
circuitry and logic in most of the medical equipment is controlled by a computer. Many
surgical procedures are computer-guided. With the help of these computers the time
required to operate a patient is drastically reduced. The functioning of hospital-bed
beeping systems, emergency alarm systems, X-ray machines and several such medical
appliances is based on computer logic. Computer software is used for diagnosis of
diseases. It can be used for the examination of internal organs of the body. Advanced
computer-based systems are used to examine delicate organs of the body. Some of the
complex surgeries can be performed with the aid of computers. The different types of
monitoring equipment in hospitals are often based on computer programming.
Personal computers (PCs) are becoming ever more popular in the medical
community as prices decrease while performance increases. In fact, many practitioners
use PCs to keep patient records and information. In light of this fact, it is obvious that PC
based signal acquisition, and analysis is an efficient and cost effective method of patient
biomedical signal acquisition and monitoring. A PC based system consists of a few
external hardware components for isolation and amplification of the signals, a data
acquisition card, and a software analysis package can bypass the need for standalone
instruments by using the PCs currently available and some inexpensive acquisition
equipment.
Biomedical signal acquisition has greatly advanced over the years, encompassing
many different technologies. With the increasing performance of the personal computer,
PC based signal processing systems are becoming an efficient and cost-effective way of
acquiring and analyzing these signals. The advanced analysis techniques available on the
PC are becoming invaluable to the practicing physician.
Biomedical signals are frequently complex waveforms that occur in a very noisy
environment. Although the information content and the best way to present this
information is under constant study in the medical and research community, advanced
signal processing techniques have proven to be invaluable to the analysis of biomedical
signals. With the increased capability of personal computers (PCs), the acquisition and
analysis of biomedical signals is becoming practical at the individual physician level.
Biomedical signals of interest are frequently in the microvolt region and in the
presence of noise that is often a hundred to a thousand times greater in amplitude. After
the signal is acquired, it is frequently enhanced to stress the relevant information. This
can involve sophisticated digital processing techniques. Data reduction techniques to
extract specific features from the signals are also frequently employed. Representation of
the features is another important consideration for effective utilization of the information.
All these requirements have led, until recently, to expensive hardware and software
systems. The cost of commercially available PC-based biomedical instrumentation
systems varies depending on the specific capabilities desired.
Computers help doctors in performing various surgeries like laparoscopic where
doctors insert the medical tools and small cameras into the patient’s body and conduct the
operations. Many hi-tech surgical machines and instruments are integrated with computer
systems so that every surgical process is monitored and recorded so that complications
are avoided. Many clinical imaging processes are conducted and examined with the help
of computers such as X-rays, CT scans, MRI scans etc. computers also play a vital role in
conducting various clinical and biological laboratory tests in hospitals that help in correct
diagnosis of the disease. Many critical patients whose heart rate, pulse rate, brain reading
etc need to be recorded and monitored continuously can be done simultaneously with the
help of computer. Importance of computers in medical field as grown up to a height so
that many patients who need to be provided with life support system are also governed by
special computerized systems.
Specially designed software and automated machines are used to treat various
diseases and disorders. Diagnostics tools like MRI, CT scans, ULTRA sound tests,
radiation technology requires computers. New computerized technologies used during
operations like laparoscopic surgeries, LASER surgeries, machine cuts, have resulted in
very short stays in hospitals post surgery and fast recovery as well.
Significance of computer technology in the health sector is actually
unquestionable when it comes to medical imaging. The technology and the instruments
have helped save life of millions of people. Various types techniques helped create
images of the human body, or body parts for medical purposes.
The modern methods of scanning and imaging like Magnetic Resonance Imaging
(MRI), Ultra Sonography, Monography, 3-D Images are based on advanced computers
technology. Storing these images is also easy with the help of computers.
Computers can also be used in the hospitals to detect the changes taking place
inside the human body. Without a cut on the skin, like bone scan, blood glucose monitor,
endoscopy, blood pressure monitor, prenatal ultra sound imaging etc. computers have
brought more precision in medical examination and diagnosis.
Importance of computers in health care is explained by their use in infertility
treatments, DNA research, Cancer treatment (radiation), treatment of premature infants,
detection of defects in the fetus in the womb, etc.
2.1.1 APPLICATIONS OF PERSONAL COMPUTERS IN BIOMEDICAL
Applications [1] of the digital computer in medical fields are so numerous. Most of
these applications, however, utilize a few basic capabilities of the computer which
provide an insight to ways in which computers can be used in conjunction with
biomedical instrumentation system. These basic capabilities include:
1. Data Acquisition: The reading of instruments and transcribing of data can be done
automatically under the control of the computer. This not only results in a substantial
saving of time and effort, but also reduces the number of errors in the data. When data are
expected at irregular intervals, the computer can continuously scan all input sources and
accept data whenever they are produced. If the originated data is in analog form the
computer usually samples, controls and processes the data in digital format. The
computer can also be programmed to reject the unwanted signals/readings so that some
sort of trouble can be avoided for the measuring instrumentation system.
2. Storage and retrieval: The ability of the digital computer to store and retrieve large
quantities of data is well known. The biomedical field provides ample of opportunities to
make use of this capability. The digital computer, however, can serve as an automated
filing system in which information can be automatically entered as it is generated. These
files can be stored as long as necessary and updated when ever appropriate. Any or all of
the information can be retrieved on command whenever desired and can be manipulated
to provide output reports in tabular or graphic form to meet the needs of the hospital staff
or other users.
3. Data reduction and transformation: The sequence of numbers resulting from digitizing
an analog physiological signal such as ECG would be quite useless if retrieved from the
computer in raw form. To obtain meaningful information from such data, some form of
data reduction or transformation is necessary to represent the data as a set of specific
parameters. These parameters can be analyzed, compared with other parameters, or
otherwise manipulated.
4. Mathematical operations: Many important physiological variables cannot be measured
directly, but must be calculated from other variables that are accessible. If a digital
computer is connected on-line with the measuring instruments, the calculated results can
often be obtained while the patient is still connected to the instruments. This not only
enables the physician to conduct further tests if the results so indicate, but can also inform
the patient immediately if any measurements were not properly made and require
repetition.
5. Pattern recognition: To reduce certain types of physiological data into useful parameters,
it is often necessary that important features of a physiological waveform or an image be
identified. Computer programs are available to search the data representing image of the
needed value.
6. Limit detection: In applications involving monitoring and screening, it is often necessary
to determine when a measured variable exceeds certain limits. By comparison of the
measured parameter with each limit of the range, the computer can indicate which
parameters exceed the limit and the amount by which they deviate from normal.
7. Statistical analysis of data: In the diagnosis of disease, it is often necessary to select one
datum most likely to cause out of as set of possible causes associated with a given set of
observed symptoms, measurements, and test results.
8. Data presentation: An important characteristic of any instrumentation and dataprocessing system is its ability to present the results of measurements and analyses to its
users in the most meaningful way possible. By virtue of appropriate output devices, a
digital computer can provide information in a number of useful forms. Table printouts,
graphs, and charts can be produced automatically, with features clearly labeled using both
alphabetic and numeric symbols.
9. Control functions: Digital computers are capable of providing output signals that can be
used to control other devices. In such applications, the computer is programmed to
influence or control physiological, chemical, or other measurements from which its input
data are being generated. The computer can also be used to provide feedback to the
source of its data.
2.2 REVIEW OF LITERATURE ON PC BASED MEDICAL INSTRUMENTS:
The economy of mass production of different systems has led to the use of the
personal computer as the central computer for many types of biomedical applications.
Many medical instrument manufacturing companies use PCs for applications as sampling
and analyzing physiological signals, maintaining equipment databases in the clinical
engineering department of hospitals, and simulation and modeling of physiological
systems.
The LINC (Laboratory INstrument Computer) was the most successful
minicomputer used for bio-medical applications.
The LINC gave the developers a
tremendous insight into what the PC should be like long before it was possible to build a
personal computer. From then PC’s were used for many applications in medical
instrumentations
[2]
(Tompkins, 1996). IBM PC was used for developing signal
processing and Artificial Neural Networks (ANN) algorithms for analysis of the
electrocardiogram (Pan and Tompkins, 1995; Hamilton and Tompkins, 1996; Xue et el.,).
These studies have also included development of techniques for data compression to
reduce the amount of storage space required to save ECG’s data.(Hamilton and
Tompkins, 1991a, 1991b). One of the applications that was developed based on Apple
Macintosh II computer is electrical impedance tomography (Yorkey et el., Woo et el.,;
Hua et el.,).
The advent of the microprocessor has markedly affected medical instrumentation,
as it has most disciplines involving measurement or control. Microprocessors are now
incorporated in many commercially available clinical instruments to enhance their
capabilities or automate their operations. In some systems, such as certain patient
monitors, microprocessors have replaced mini computers, substantially reducing their
costs.
Most medical applications of computers and microprocessors involve specific
instrumentation systems; in fact the computer often becomes an integral part of an
instrument. It is therefore essential that anyone involved in the field of medical
instrumentation be familiar with the basic concepts of digital computation and some of
the more important medical applications. Furthermore, it is important that the biomedical
engineer or technicians be given an understanding of the techniques involved in
interfacing a computer with the rest of the instrumentation system.
The use of computers for the clinical analysis of the electrocardiogram (ECG) has
developed over a span of many years. First, ECG potentials are relatively easy to
measure. Second, the ECG is an extremely useful indicator for both screening and
diagnosis of cardiac abnormalities. Measurement of the electrocardiogram for computer
analysis is essentially the same as used for manual ECG interpretation.
Modern clinical laboratory includes various types of automated instruments for
the routine analysis of blood, urine, and other body fluids and tissues. Another feature of
the most clinical laboratory computer systems is the capability of handling emergency
request.
The physiological variables typically measured by a patient-monitoring system
include the ECG, temperature, a means of obtaining respiration rate, and often arterial
and central venous blood pressures. Blood gas and pH measurements are also sometimes
included. In a computerized system, the computer generally controls the collection and
logging of data from their various sources to assure that readings are taken at the required
intervals and properly recorded.
Data reduction and transformation techniques and mathematical operations are
employed extensively in the calculation of a number of parameters, and many of them are
indirect. The derived parameters usually include heart rate, respiration rate systolic and
diastolic blood pressures, and mean arterial and venous pressures. Other parameters, such
as cardiac output, stroke volume, blood gas values, urine output, and various lung
volumes and capacities are also sometimes calculated. In some cases, a computer can also
be used to control the infusion of blood or medication, based on the measured values of
affected variables.
A highly acclaimed application of the computer to clinical medicine is
computerized axial tomography (CAT). The procedure combines X-Ray imaging with
computer techniques, permits visualization of internal organs and body structures with
greater definition and clarity than could ever be obtained by conventional methods.
Although the proliferation of computers has extended into almost all types of
medical instrumentation, a few of the applications are pulmonary function laboratory,
pulmonary function tests and arterial blood gas analysis. Measured values of lung
volumes, vital capacity, flow rates, FEVs, blood gas levels, and related variables are
compared with predicted normal values, based on the height, weight of the patient.
The success of computerized axial tomography to obtain detailed X-ray images of
slices of the body has led to the development of similar techniques for other forms of
imaging called emission computerized tomography. An application of computerized
tomographic techniques to nuclear medicine, which permits detailed visualization of the
distribution of radioisotopes throughout the body.
Computerized tomographic methods are being developed for ultrasonic imaging of
the heart and abdominal organs. Computer techniques are also involved in
zeugmatography, a new noninvasive imaging method utilizing the measurement of
nuclear magnetic resonance (NMR). The benefits to be obtained from these and other
computer applications in medical technology must yet to be assessed in light of their
costs before their clinical significance can be determined.
2.3 REVIEW OF LITERATURE ON PC BASED PCG, ECG, PULSE OXIMETER,
TEMPERATURE MEASURMENRT AND HUMIDITY:
2.3.1 PHONOCARDIOGRAPH:
There are advanced signal processing technologies that hold promise for
developing computer-aided auscultation solutions that are intuitive, efficient, informative
and accurate. Computer-aided auscultation [3] offers an objective, quantitative and costeffective tool for acquiring and analyzing heart sounds, providing archival records that
support the patient evaluation and referral decision as well as serial comparisons for
patient monitoring. There is the further promise of new quantitative acoustic measures
and auscultatory findings that have more precise correlation with underlying
physiological parameters. These solutions are being developed with the benefits of a rich
literature of clinical studies in phonocardiography, the added insights derived from
echocardiography, and advances in signal processing technology.
Reza R Sarbandi et al
[4]
developed a method to analyze phonocardiogram. They
used colour spectrographic technique. It is a technique recommended for amplifying noninvasive cardiac monitoring.
Nazeran H et al
[5]
Wavelet-based segmentation algorithm is quite effective in
localizing the important components of both normal and abnormal heart sounds. They
demonstrated that wavelet-based analysis, extracting vectors which are clearly
differentiable for automatic classification of heart sounds.
Obaidat MS. et al
[6]
presented the applications of the spectrogram, Wigner
distribution and wavelet transform analysis methods to the phonocardiogram (PCG)
signals. A comparison between these three methods has shown the resolution differences
between them. It is found that the wavelet transform is capable of detecting the two
components, the aortic valve component A2 and pulmonary valve component P2, of the
second sound S2 of a normal PCG signal. Furthermore, the wavelet transform provides
more features and characteristics of the PCG signals that will help physicians to obtain
qualitative and quantitative measurements of the time-frequency characteristics.
Durand LG, Pibarot P., et al
[7]
presented application of spectral analysis and the
potential of new time-frequency representations and cardiac acoustic mapping to resolve
the controversies and better understand the genesis and transmission of heart sounds and
murmurs within the heart-thorax acoustic system are reviewed. The most recent
developments in the application of linear predictive coding, spectral analysis, time-
frequency representation techniques, and pattern recognition for the detection and followup of native and prosthetic valve degeneration and dysfunction are also presented in
detail. New areas of research and clinical applications and areas of potential future
developments are then highlighted.
L.Khadra, M. Matalgah, B. El-Asir and S. Mawagdeh et al
[8]
used
wavelet
transform, which is the decomposition of a signal into a set of independent frequency
channels, is shown to be a useful diagnostic tool in the analysis of heartbeat sounds. In
particular, the wavelet transform enables the experimentalist to obtain qualitative and
quantitative measurements of time- frequency characteristics of phonocardiogram (PCG)
signals.
Tranulis C, Durand LG, Senhadji L, Pibarot P. et al
[9]
Developed a method to
conduct a study on non-invasive method for the estimation of pulmonary arterial pressure
(PAP) using a neural network (NN) and features extracted from the second heart sound
(S2).
Lin Z, Chen JD. et al
[10]
developed a method to map a one-dimensional function
of time (or frequency), the time-frequency representation can localize the signal energy in
both the time and frequency directions. It has been shown that many biomedical signal
problems may benefit from time-frequency analysis. The objective of this paper is to
review the advances in time-frequency analysis of biomedical signals. Relevant
theoretical methodologies and practical considerations are introduced, and five
application areas are reviewed: electroencephalography (EEG), electrocardiography,
phonocardiography, electrogastrography, and electromyography.
Recently, Noponen et al. [11] studied the phono-spectrographic features of heart
murmurs to investigated and to distinguish innocent murmurs from pathological murmurs
in children.
Kudriavtsev et al. [12] developed an approach based on spectrograms to present an
intensity/frequency image of heart sound properties over a period of time, which they
called heart energy signature spectrogram.
2.3.2 ELECTROCARDIOGRAPH:
An Electrocardiogram (ECG) is a test used to determine the regular rhythmic
activity of the heart condition. This activity is recorded on graph sheets or some kinds of
monitors by placing the electrodes on specific locations of the body of a person. The
record shows a series of electrical waves that occur during each beat of the heart. The
recorded waves have peaks and valleys and normally represented by the letters P, Q, R,
S, T and U waves.
Gordan Cornelia et al
[13]
used wavelet transforms as tool for processing non
stationary signals like ECG signals.
R.F.Von Borries et al [14] developed two channel filter banks to remove effectively
the base line drift and S.Z.Mahmoodabadi et al
[15]
demonstrated the filtering of ECG
signal by using Db4 and Db6 at higher scales to preserve various components of ECG
signal.
The distortion of R morphology occurs in classical wavelet approach and this
drawback is removed by A Choukari et al
[16]
by applying their algorithm on detail
coefficients of both noise free ECG signal and ECG signal corrupted with WGN. The
authors claimed that the performance of their algorithm is superior compared to classical
wavelet transform in restoring P and T waves without distorting R morphology. But the
limitation is that it heavily depends upon the presence of the R waves in the first level of
approximation of the noisy ECG signal.
S.A.Choukari et al
[17]
used second level decomposition for detecting QRS
complex and fourth and fifth level of decomposition for detecting P and T waves.
M.Kania et al
[18]
, studied the importance of the proper selection of mother
wavelet with appropriate number of decomposition levels for reducing the noise from the
ECG signal. The authors claim that they obtained good quality signal for the wavelet db1
at first and fourth level of decomposition and sym3 for the exclusive study of fourth level
of decomposition.
D.T Ingole et al
[19]
used Dyadic wavelet Transform for extraction of ECG
features, which is robust, highly efficient, accurate and reliable
Tan Yun-fu et al
[20]
used Daubechies and symlet wavelets for the removal of
various kinds of noises present in the ECG signal and reconstructed ECG signal with
minimum distortion at faster rate.
Abdel-Reman at el
[21]
used the high pass filtering for noisy signal before
reconstructing it by inverse discrete wavelet transform (IDWT).
Naregalkar Akshay et al
[22]
demonstrated the application of UWT for the
removal of base line wonder and QRS morphology detection in LABVIEW environment.
Antonio et al
[23]
used wavelet transform to detect the R-wave and wavelet
segmentation approach for the extraction of ECG features.
[24]
Pramodkumar et al
, compared the performance of DWT and SWT for
denoising the ECG using three different threshold methods namely universal (sqtwolog),
minimax and heursure threshold methods.
F.Abdelliche et al
[25]
examined the fractional wavelet transform by choosing the
cole-cole function as mother wavelet to detect the QRS complex, buried in various kinds
of noises and the results are comparable to the other types of the techniques .Yet there is
a scope to develop the detection algorithm for the complete use of the data base.
Yang Ying et al [26], proposed a new shrinkage function for denoising ECG signal
and compared the results with various shrinkage functions
Zaffery Z.A et al
[27]
have evaluated the performances of four different
threshold estimators rules in the application of denoising the ECG signal in the
MATLAB7 environment
Xiao-li Yang et al
[28]
proposed Hilbert-Huang Transform which consists of
computation of Hilbert transform and the empirical mode of decomposition of series of
narrow band signals obtained from the decomposition of original signal based on
instantaneous frequency concept to detect R-wave in presence of various noises and
artifacts
Mohamed O.Ahmed Omar et al
[29]
applied morphological based approach for
feature extraction of ECG signal and curve fitting algorithm for the removal of base line
wander.
R.Shantha Selva kumari et al [30] designed two wavelets W1 and W2 which satisfy
perfect reconstruction condition for the removal of base line wander from the cardiac
signal and compared it with existing wavelets namely db4, bior 4.4 and bior 6.8.
G Ranganathan et al
[31]
presented HRV analysis for evaluation of mental stress
assessment using Dyadic wavelet transform.
2.3.3 PULSE OXIMETER:
The pulse oximeter is one of the most important advances in noninvasive
monitoring because it provides a means of continuously and quickly assessing arterial
blood oxygenation. Today's pulse oximeter owes a good measure of its success to the
technologic advances in light emission and detection and the ready availability of
computers and their software.
Pulse oximetry is based on two physical principles: (a) the presence of a pulsatile
signal generated by arterial blood, which is relatively independent of non-pulsatile
arterial blood, venous and capillary blood, and other tissues; and (b) the fact that
oxyhemoglobin (O2Hb) and reduced hemoglobin (Hb) have different absorption spectra.
Pulse oximetry is probably one of the most important advances in respiratory
monitoring. Over the last 15 years, numerous studies have focused on the technical
aspects of pulse oximeters and found that these instruments have a reasonable degree of
accuracy. This degree of accuracy, coupled with the ease of operation of most
instruments, has led to the widespread use of pulseoximetry for monitoring patients in the
ICU. Perhaps the major challenge facing pulse oximetry is whether this technology can
be incorporated effectively into diagnostic and management algorithms that can improve
the efficiency of clinical management in the intensive care unit.
The first pulse oximeter was designed in the late 1930’s by German researchers
whose objective was to measure the oxygenation of “high altitude pilots”
[32]
. From that
point the pulse oximeter, as it was later called, has been improved upon continuously.
Kramer
[33]
, Matthes
[34]
developed the 2-wavelength ear O2 saturation meter
with red and green filters, later switched to red and infrared filters in their own ways.
Wood
[35]
added a pressure capsule to squeeze blood out of ear to obtain zero
setting in an effort to obtain absolute O2 saturation value when blood was readmitted. The
concept is similar to today's conventional pulse oximetry but suffered due to unstable
photocells and light sources. This method is not used clinically.
Shaw assembled the first absolute reading ear oximeter by using eight
wavelengths of light. Commercialized by Hewlett Packard
[36]
, its use was limited to
pulmonary functions and sleep laboratories due to cost and size.
Aoyagi
[37]
developed Pulse oximetry at Nihon Kohden using the ratio of red to
infrared light absorption of pulsating components at the measuring site. It was
commercialized by Biox and Nellcor. Biox was founded in 1979, and introduced the first
pulse oximeter to commercial distribution in 1981. Biox initially focused on respiratory
care, but when the company discovered that their pulse oximeters were being used in
operating rooms to monitor oxygen levels, Biox [1982] expanded its marketing resources
to focus on operating rooms. Nellcor
[38]
Incorporated in 1982, and began to compete
with Biox for the US operating room market in 1983. Prior to its introduction, a patient's
oxygenation was determined by a painful arterial blood gas, a single point measure which
typically took a minimum of 20-30 minutes processing by a laboratory. In the absence of
oxygenation, damage to the brain starts in 5 minutes with brain death in another 10-15
minutes. With the introduction of pulse oximetery, a non-invasive, continuous measure of
patient's oxygenation was possible, revolutionizing the practice of anesthesia and greatly
improving patient safety.
By 1987, the standard of care for the administration of a general anesthetic in the
US included pulse oximetery. From the operating room, the use of pulse oximetery
rapidly spread throughout the hospital, first in the recovery room, and then into the
various intensive care units. Pulse oximetery was of particular value in the neonatal unit
where the patients do not thrive with inadequate oxygenation, but also can be blinded
with too much oxygen. Furthermore, obtaining an arterial blood gas from a neonatal
patient is extremely difficult.
Masimo, a California-based company, introduced the first pulse oximeter able to
provide accurate measurements during periods of patient’s motion or low peripheral
perfusion, long thought to be limitations of pulse oximetery technology that could not be
overcome. The ability to provide accurate measurements under these difficult clinical
conditions meant pulse oximetry could be used outside the operating room, where
patients were generally well perfused and not moving, allowing for adoption in neonatal
intensive care units, ambulances, and other challenging settings.
By 2008, the accuracy and capability of Pulse Oximetry had continued to
increase, and had allowed for the adoption of the term High Resolution Pulse Oximetry
(HRPO). One area of particular interest in the area of Pulse Oximetry is the use of Pulse
Oximetry in conducting portable and in-home sleep apnea screening and testing.
2.3.4 HUMIDITY AND TEMPERATURE
Humidity is the level of moisture that is contained in the air. It is actually "water
vapour", and cannot be seen, but humans can identify high and low humidity, because the
air is noticeably damp. Our natural perspiration that evaporates quickly from our bodies
in an atmosphere of low humidity causes us to sweat and become "sticky" when humidity
is high. In cooler air, high humidity causes us to feel damp and cold. There are three main
measurements of humidity: absolute, relative and specific.
The earlier method employed for measurement of humidity and temperature
measurement utilized a variety of sensors for the detection of temperature in the
respiratory tract. In 1928 Perwitzschky used mercury thermometers
[39, 40]
for the
evaluation of nasal temperature. The investigators that followed performed studies using
thermistors
[41, 42, 43, 44, and 45].
In rhinologic research the thermocouples were frequently
used due to their small size and very fast response time [46, 47, 48, 49, 45, 50, and 51].
According to results from a study by Keck et al, the anterior segment of the nasal
cavity appears to have a major contribution in nasal air conditioning capacity
[48, 49]
.
Between the surface of the nasal mucosa and the inhaled air exists a temperature
difference. This is important for sufficient heat exchange between the respiratory mucosa
and the inhaled air. During inspiration the warmer nasal walls warm the cooler
environmental air, while during expiration the cooler walls cool down the expired air
from the lungs. Accordingly, the lowest temperature of the nasal mucosa can be
measured at the end of inspiration while the highest mucosal temperature is at the end of
expiration. The exchange of heat and moisture between mucosa and air also depends on
the nasal airflow patterns. At intranasal regions with turbulent airflow, the temperature
changes during the respiratory cycle are higher than in segments with dominating laminar
airflow
[52]
. The temperature of the nasal cavity wall mucosa varies both with location
and with time through the respiratory cycle. Keck et al detected mean temperature values
at the end of inspiration in different locations within the nasal cavity [48, 49]. In the nasal
vestibule the mean end-inspiratory temperature was 25.3◦C, 29.8◦C in the nasal valve
area; in the anterior turbinate area (near to the head of the middle turbinate) was 32.3◦C
and 33.9◦C in the nasopharynx. The mean temperature of the nasal mucosa during
respiration ranges from 30.2 to 34.4 ◦C [52]
2.4 REVIEW OF LITERATURE ON LabVIEW BASED MEDICAL
INSTRUMENTS:
Biomedical engineering is one of the fields in which virtual instrumentation has
penetrated rapidly and strongly, into both research endeavors and current use of
equipments. An important note must be taken towards the fact that virtual
instrumentation will not replace traditional instruments entirely, especially in highly
specialized domains. In the case of many applications, combined solutions are preferred,
based on both measuring techniques and providing the advantages stemming from these.
Virtual instruments use the open architecture of regular computers, including their
processing speeds, memory and display capabilities together with inexpensive interface
boards, connected to the appropriate bus, and the renowned connectivity of such
computers for creating equipments that are efficient, reusable and re-configurable. The
end result is a piece of virtual equipment whose performances, uses and configurations
are set and defined by the user. The advantage of virtual instruments over traditional
instruments will continue to rise due to fast developments in the world of PC
technologies. The major benefit resides in the increase of performance, coupled with a
sharp decrease in implementation costs.
A further characteristic feature of virtual instruments that needs to be underlined
is the possibility to use the same computer for different virtual instruments (of course,
with the appropriate interface), and to transfer the data acquired with one such instrument
to the other, at the software level. The emergence of this concept was chiefly determined
by the intrinsic limitations of any closed type architecture, represented in the case of
biomedical engineering by classical box-like instruments. The functionality of the latter is
defined by the producer, limiting both the possibilities for expanding the diagnostic or
intervention process, and the performance the user may desire at a certain point. The
upgrading process, which in the case of computers is easy, cheap and accessible, can be
difficult or even impossible for classical instruments.
Virtual medical instrument refers to a system that integrates software and
hardware to form a usable medical device on computer [53]. As Personal Computers (PC)
are becoming more affordable, the usages of virtual instruments are increasing drastically
in biomedical field. The most popular software used to create virtual instrument in
general is National Instrument (NI) LabVIEW. It provides user friendly interface and
extensive tools to interface, model, process and display virtually any instrument.
M.Lascu in [54] and B.Grinstead in [55] used data acquisition card (DAQ) to acquire
biomedical signal from a patient’s body and utilized LabVIEW software to process the
signal to obtain usable data. Medical data such as Electrocardiogram was displayed on a
virtual medical instrument.
T.Xin in
[56]
created a mixed type wireless telemedicine monitor centre system
using LabVIEW, general packet radio service (GPRS) and Digital Signal Processor
(DSP). A telemedicine central unit receives patient vital signs which were captured and
transmitted by a PDA over the internet using GPRS and also by DSP boards over LAN.
A study by Y.Lin described an automatic instrumental reading system using
CMOS camera and neural network system to do optical character recognition on an
ocular optical instrument [57]. In conversion 2011 International Conference on Biomedical
Engineering and Technology IPCBEE vol.11 (2011) © (2011) IACSIT Press, Singapore
70 algorithms together with self-learning back propagation neural network were created
to process the image captured. R.P.Ghugardare in
[58]
did a similar research to obtain
seven-segment display reading using OCR. Both these systems are very hard to be
implemented on other application as it will require the algorithm to be rewritten.
Research done by I.Lita in
[59]
and Y.Zhong in
[60]
share the similarity in which
both of them used webcam and LabVIEW Vision module to detect moving objects. The
quality of images from a normal webcam is sufficient for LabVIEW to process and result
obtained by both research proved that LabVIEW Vision module is very advance in term
of features and at the same time very easy to use.
Research in [55], [56], [57], [59], and [60] proved that LabVIEW is easy to utilize yet can
produce advance virtual instrument that is superior to traditional medical instrument
REFERENCES
1. Biomedical Instrumentation and measurements – Leslie Cormwell, Fred J.Weibell, Erich
A. Pfeiffer. – PHI publisher- Second Edition.
2. Tompkins WJ. Department of Biomedical Engineering, University of WisconsinMadison, 1550 Engineering Drive, Madison, WI 53706, USA.
3. Computer-aided auscultation of the heart: from anatomy and physiology to diagnostic
decision support. By: Watrous RL. Zargis Medical Corp., Princeton, NJ 08540, USA.
4. A color spectrographic phonocardiography (CSP) applied to the detection and
characterization of heart murmurs:
by: Reza R Sarbandi, John D Doyle, Mahdi
Navidbakhsh, Kamran Hassani and Hassan Torabiyan.
5. Wavelet-based segmentation and feature extraction of heart sounds for intelligent PDAbased phonocardiography. By Nazeran H. Electrical and Computer Engineering, The
University of Texas at El Paso, El Paso, Texas 79968, USA.
6. Phonocardiogram signal analysis: techniques and performance comparison. Department
of Electrical Engineering, City University of New York, City College, NY 10031.
7. Digital signal processing of the phonocardiogram: review of the most recent
advancements. Laboratory of Biomedical Engineering, Institute and research clinic,
Montréal, Québec, Canada.
8. The wavelet transform and its applications to phonocardiogram signal analysis: 1991,
Vol. 16, No. 3, Pages 271-277
9. Estimation of pulmonary arterial pressure by a neural network analysis using features
based on time-frequency representations of the second heart sound. Laboratoire de génie
biomedical, Institute de recherches clinics de Montréal, Quebec, Canada.
10. Advances in time-frequency analysis of biomedical signals. Lin Z, Chen JD. Department
of Medicine, University of Kansas Medical Center, Kansas City 66160-7350, USA.
11. Noponen AN, Lukkarinen S, Angerla A, Sepponen R: V Phono-spectrographic analysis
of heart murmurs in children. BMC Pediatrics 2007, 7:23
12. Kudriavtsev Vladimir, Polyshchuk Vladimir, Roy DouglasL: Heart energy signature
spectrogram for cardiovascular diagnosis. Biomedical Engineering Online 2007, 6:16
13. Gordan,Cornelia,ReizRomulus,”ECG Signal processing using wavelets”
14. R.F.Von Borries, J.H.Pierluissi and H.Nazeran, “Wavelet Transform based ECG Base
line drift Removal for body surface potential Mapping”, 27th annual conference on
Engineering in Medicine and Biology, China, IEEE ,pp.3891 – 3894, sep.1-4,2005.
15. S.Z.Mahmoodabadi,A.Ahmadian,M.D.Abolhasani, “ECG feature Extraction using
Daubechies Wavelets”, Proceedings of the fifth International conference on
Visualization, Imaging and Image Processing, pp.343-349, Sep.7-9,2005
16. S A Chouakri,F Bereksi-Reguig,S Ahmaidi,O Fokapu, “Wavelet Denoising of the
Electrocardiogram Signal based on the Corrupted noise estimation”, computers in
cardiology, IEEE vol.32,pp 1021-1024 , 2005.
17. S.A.Choukari, F.Bereksi-Reguing, S.Ahmaidi and O.Fokapu, “ECG signal smoothing
based on combining wavelet denoising levels”. Asian Journal of Information Technology
5(6), pp 666 - 77, 2006.
18. M.Kania,M.Fereniec,R.Maniewski,“Wavelet Denoising for Multi lesd High Resolution
ECG Signals”, Measurement Science Review,vol.7,Section 2, No.4,pp.30-33,2007
19. D.T Ingole, Kishore kulat, M.D.Ingole, “Feature Extraction via Multiresolution Analysis
for ECG Signal”, International Conference on Emerging trends in Engineering and
Technology, IEEEpp.659 – 664, 2008
20. TAN Yun-fu,Du Lei, “Study on Wavelet Transform in the Processing for ECG Signals”,
World congress on Software Engineering, IEEE, p 515-518, 2009
21. Abdel-Rehman Al-Quwasmi and Khaled Daqrouq, “ECG signal enhancement using
Wavelet Transform”,WSEAS Transaction on Biology and Biomedicine, issue 2,vol. 7,
pp-62-72,April 2010
22. Naregalkar Akshay, Naga Ananda Vamse and Naga Deepti Yeddanapudi, “ ECG noise
Removal and QRS complex detection using UWT”, International Conference on
Electrical and Information Engineering (ICEIE), IEEE vol.2,pp.438-442, 2010
23. Antonio Espiritu-Santo-Rincon and Cuauhtemoc, “ECG Feature Extraction via
Waveform segmentation”, 7th International Conference on Electrical Engineering,
Computing Science and Automatic Control(CCE), IEEE pp.250-255, 2010
24. Pramodkumar,
Dewanjali
Agnihotri,
“Biosignals
Denoising
via
Wavelet
Thresholds”,IETE journal of Research, vol 56, issue 3,pp.132-138,May-June 2010
25. F.Abdelliche, A.Charef, M.L.Talbi,M.Benmalek, “A Fractional wavelet for QRS
Detection”, 0-7803-9521-2/06, IEEE pp.1186-1189, 2006
26. Yang Ying,Wei Yaseen, “New Threshold and Shrinkage function fot ECG Signal
Denoising based on wavelet Transform” , IEEE ,pp.1-4, 2009.
27. Jaffery.Z.A., Ahmad.K., Afroz,”Performance comparision of Wavelet Threshold
Estimators for ECG Signal denoising”, Computing society, IEEE pp.248-251, 2010.
28. Xiao-Li Yang, Jing-tian Tang, “Hilbert-Huang Transform and Wavelet Transform for
ECG Detection”, IEEE 78-1-4244-4/08,pp.1-4, 2008.
29. Mohamed
O.Ahmed
Omar,Nahed
H.Solouma,Yasser M.Kadah,
“Morphological
Characterization of ECG Signal Abnormalities:A new approach”, Proceedings Cairo
International Biomedical Engineering Conference, pp.1-5,2006
30. R.Shantha selva Kumari,S.bharathi,V.Sadashivam, “Design of Discrete Wavelet for ECG
Signal using orthogonal filter bank”, International Conference on Computational
intelligence and Multimedia applications, Computer Society IEEE,pp.525-529, 2007.
31. G.Ranganathan,V.Bindhu,R.Rangarajan, “ECG Signal Processing using Dyadic Wavelet
for mental stress assessment”, IEEE,978-1-4244-4713-8/10,2010
32. Yun-Thai Li, “ Pulse Oximetry “,Department of Electronic Engineering, University of
Surrey, Guildford, GU2 7XH
33. Tytler JA, Seeley HF. The Nellcor N-101 pulse oximeter. A clinical evaluation in
anaesthesia and intensive care. Anaesthesia 41: 302–305, 1986.
34. Matthes, K., Untersuchungen uber die sauerstoffsattigungen menschlichen arterienblutes,
Arc. Exp. Pathol. Parmakol. 179, 689, 1935.
35. Wood, E. and Geraci, J.E., Photoelectric determination of arterial oxygen saturation in
man, J. Lab Clin. Med., 34, 387, 1949.
36. Merrick, E.B. and T.J. Hayes, continuous, non invasive measurements of arterial blood
oxygen level, Hewlett-Packard J., p 2 Oct.1976.
37. J Aoyagi, T., Kishi,T., Yamaguchi, K., and Watanabe,S., Improvement of ear piece
oximator,in Proc. 13th Japan. Medical Electronics and Biological Engineering Conf.,
Osaka 90,1974.
38. Tytler JA, Seeley HF. The Nellcor N-101 pulse oximeter. A clinical evaluation in
anaesthesia and intensive care. Anesthesia 41: 302–305, 1986.
39. Perwitzschky, R., 1928. Die Temperature- und Feuchtigkeitsverh¨altnisse der Atemluft in
den Luftwegen. I. Mitteilung. Arch. F. Hals-, Nasen- u Ohrenheilk 117, 1–36.
40. Perwitzschky, R., 1930. Die Temperatur- und Feuchtigkeitsverh¨altnisse der Atemluftin
den Luftwegen. II. Mitteilung. Arch. F. Hals-, Nasen- u Ohrenheilk. 125, 1– 22.
41. G Cole, P., 1954. Recordings of respiratory air temperature. J. Laryngol. Otol. 68, 295–
307.
42. Cole, P., 1954. Respiratory mucosal vascular responses, air conditioning and
thermoregulation. J. Laryngol. Otol. 68, 613–622.
43. McFadden Jr, E.R., Pichurko, B.M., Bowman, H.F., Ingenito, E., Burns, S., Dowling, N.,
Solway, J., 1985. Thermal mapping of the airways in humans. J. Appl. Physiol. 58, 564–
570.
44. Primiano Jr., F.P., Montague Jr., F.W., Saidel, G.M., 1984. Measurement system for
respiratory water vapor and temperature dynamics. J. Appl. Physiol. Respir. Environ.
Exerc. Physiol. 56, 1679–1685.
45. Rouadi, P., Baroody, F.M., Abbott, D., Naureckas, E., Solway, J.,Nacleiro, R.M., 1999.
A technique to measure the ability of the human nose to warm and to humidify air. J.
Appl. Physiol. 87, 400–406.
46. Ingelstedt, S., 1956. Studies on the conditioning of air in the respiratory tract. Acta
Otolaryngol. (Suppl. 131), 1–80.
47. Kaufman, J.W., Farahmand, K., 2006. In vivo measurements of human oral cavity heat
and water vapor transport. Respir. Physiol. 150, 261– 277.
48. Keck, T., Leiacker, R., Riechelmann, H., Rettinger, G., 2000a. Temperature profile in
the nasal cavity. Laryngoscope 110, 651–654.
49. Keck, T., Leiacker, R., Heinrich, A., K¨ uhnemann, S., Rettinger, G., 2000b. Humidity
and temperature profile in the nasal cavity. Rhinology 38, 167–171.
50. Seeley, L.E., 1940. Study of changes in the temperature and water vapor content of
respired air in the nasal cavity. Heat Piping Air Cond. 12, 377–388.
51. Webb, P., 1951. Air temperatures in respiratory tracts of resting subjects in cold. J. Appl.
Physiol. 4, 378–382
52. Lindemann, J., Leiacker, R., Rettinger, G., Keck, T., 2002. Nasal mucosal temperature
during respiration. Clin. Otolaryngol. 27, 135–139.
53. L. Chengwei, Z. Limei, and H. Xiaoming, “The Study on Virtual Medical Instrument
based on LabVIEW.” Conference proceedings: Annual International Conference of the
IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and
Biology Society. Conference, vol. 4, Jan. 2005, pp. 4072-5.
54. M. Lascu and D.A.N. Lascu, “LabVIEW Based Biomedical Signal Acquisition and
Processing,” Image (Rochester, N.Y.), 2007, pp. 38-43.
55. B. Grinstead and M.E. Parten, “Biomedical signal acquisition using "LabVIEW"”
Proceedings. 11th IEEE Symposium on Computer-Based Medical Systems (Cat.
No.98CB36237), pp. 157-161.
56. T. Xin, G. Xing-ming, and C. Min, “Design and Realization of Distributed Wireless
Telemedicine Monitor Center Based on Virtual Instruments,” 3rd International
Conference on Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 2009,
pp. 1-4.
57. Y. Lin, “Study on the Automatic Instrumental Reading System Based on Image
Processing,” 2007 International Conference on Mechatronics and Automation, Aug.
2007, pp. 3870-3875.
58. R.P. Ghugardare, S.P. Narote, P. Mukherjee, and P.M. Kulkarni, “Optical character
recognition system for seven segment display images of measuring instruments,”
TENCON 2009 - 2009 IEEE Region 10 Conference, Nov. 2009, pp. 1-6.
59. I. Lita, D.A. Visan, and I.B. Cioc, “LabVIEW Application for Movement Detection
Using Image Acquisition and Processing,” IEEE 16th International Symposium for
Design and Technology in Electronic Packaging (SIITME), 2010, 2010, pp. 225-228.
60. Y. Zhong, “Research on network of remote real-time surveillance system based on
LabVIEW,” 2009 7th IEEE International Conference on Industrial Informatics, Jun.
2009, pp. 60-65.