Dear Colleague:
I am enclosing the detailed part of the tutorial talks and
some of the medical applications that will be presented by Drs. Akay,
Hudson, Cohen and Cios.
In addition to these, we will have other medical and biological
applications which will be presented by invited speakers in the
field.
Note that the workshop will be held Nov 1st and 2nd which is before
the Conference. The fee for the workshop is
IEEE Member: $250
Others: $330
The conference will start Nov 3 and end Nov 6. Attendees to the
workshop do not have to continue with the conference, although I
highly recommend it. Note that the conference will require a separate
attending fee.
Please do not hesitate to call me if you have any questions
regarding the workshop. I can be reached at (908) 445-4906
or (akay at gandalf.rutgers.edu).
I am looking forward to hearing from you.
Sincerely,
Metin AKAY, Ph.D.
Phone: (908) 445-4906
e-mail: akay at gandalf.rutgers.edu
WORKSHOP
Fuzzy Logic in Medicine and Biology
First Day
Participation: Dr. Metin Akay , Rutgers University
Dr. Donna Hudson, UCSF
Dr. Maurice Cohen, UCSF
Dr. Krzysztof Cios, Univ Of Toledo
Fundamentals of Fuzzy Logic
Imprecisely defined classes play an important role in human thinking.
Fuzzy set theory derives from the fact almost all natural classes and
concepts are Fuzzy rather than crisp in nature. According to
Lotfi Zadeh, who is the founder of fuzzy logic, all of the
reasoning people use everyday is Approximate in nature. Humans
work from approximate data, extract meaningful information from massive
data, and find crisp solutions. Every moment, for example, the
photoreceptors of our retina are bombarded with millions of light
rays, and we perceive and understand the total picture as clear and
sunny day. The fuzzy logic provides a suitable basis for the ability
to summarize information, to extricate from the collections of masses
of data impinging upon the human brain those and only those which are
relevant to performance of the task at hand.
In this workshop, the concepts of fuzzy logic including the fuzzy
logic controller, fuzzy pattern recognicition systems, the fuzzy expert
system, and fuzzy neural networks for biological control systems,
pattern recognition and expert systems will be discussed in details. The
implementation of the Fuzzy logic will discussed. Then, the advantages
of the fuzzy logic over the classical classification techniques will
be discussed.
Fuzzy Logic in Biological Control Systems
The purpose of pattern recognition is a search for structure in data
which, we hope and assume, carries
information about the process generating them.
One of the means of achieving it is to use clustering where we want
to partition data into a
certain number of "natural" subsets where the elements of each set are
as similar as possible to each other and as different as possible from
those of the other sets. Here, we shall discuss the fuzzy c-means
clustering algorithm and its variations including algorithms
which are able to recognize shape of clusters. In addition, we
shall talk about cluster validity, i.e. how to check
whether the discovered structure of the data is the "correct" one.
Fuzzy Logic in Biological Pattern Recognicition
The purpose of the pattern recognition is to partition data into a
certain number of natural subsets where the elements of each set are
as similar as possible to each other and as different as possible from
those of the other sets. Here, we will discuss the fuzzy
clustering algorithms including fuzzy c-means, fuzzy c-shells
algorithm with classifier design.
Probabilistic Reasoning and of Uncertainty Managements with
Biomedical Applications
In medical applications, the relationships are often inexact and the
conclusions are uncertain. Reasoning with such imprecise information,
the physician has to make a decision about the status of patient. For
these reasons, the use of probabilistic techniques to create the
medical expert systems which perform uncertainty reasoning is one
of the important research areas.
Although the need for handling of uncertainty in computer models has
been recognized since the beginning of computer modeling, early
attempts used mainly adhoc approaches with minimal theoretical
foundations. At the same time, theoretical foundations of fuzzy logic
and other techniques of approximate reasoning were under development.
For a number of years, these areas of research progresses separately
with little interaction. In the last decade theoretical developments
have been combined with practical implementations to provide systems
with sophisticated techniques for handling uncertainty. The evolution
of these methods will be discussed.
A number of approaches to the handling of uncertainty exist, including
certainty factors, probabilistic interference, Dempster-Shaffer
inference, fuzzy logic, and approximate reasoning. These methods will
be compared, contrasted and illustrated with concrete examples. A
number of fuzzy logic techniques will be illustrated, including fuzzy
numbers applied to test results, fuzzy matching to determine degree of
normalcy, the use of membership functions, methods for determination
of appropriate membership functions, and the types and uses of
linguistic quantifiers.
Fuzzy Expert Systems with Medical Applications
In the development of expert systems, a number of components must be
considered as sources of uncertainty. These include the knowledge
base, the patient case information, the reasoning process, and the
evaluation of results. Implications of uncertainty in each of these
areas will be discussed, along with alternative methods for dealing
with each.
Fuzzy logic techniques in expert systems will be illustrated in
detail, including the design of fuzzy knowledge bases, handling of
uncertainties in patient data. and implementation of fuzzy inference.
Participants will have an opportunity to use a fuzzy medical expert
system for analysis of chest pain to see how these techniques are
utilized in practice.
Knowledge-based fuzzy expert systems will be compared with other
approaches, including connectionist systems, Bayesian systems,
and hybrid approaches. Methods for defining membership functions
in knowledge-based systems will also be presented.
Fuzzy Logic in Nonlinear Systems with Biomedical Applications
Approaches for inclusion of chaotic data in expert systems will
be discussed, along with general issues in soft computing which
focus on approximate solutions to practical problems rather
than on the development of precise mathematical models which
produce intractable solutions.
A number of the fuzzy logic approaches to nonlinear systems
will be illustrated for a number of medical applications including
diagnosis in cardiology, noninvasive detection coronary artery disease,
development of prognostic models in melanoma, and analysis of test
results in lung cancer.
SECOND DAY
APPLICATIONS OF FUZZY LOGIC TO MEDICINE AND BIOLOGY
Participation: Dr. Metin Akay (Chair), Rutgers University
Dr. Donna Hudson, UCSF
Dr. Maurice Cohen, UCSF
Dr. Larry Hall, USF
Dr. Howard Jay Chizeck, Case Western
Dr. Patrick Eklund, Finland
Dr. Safwan Shah, Univ of Colorado, Boulder
Dr. Krzysztof Cios, Univ Of Toledo
Dr. Bhavin V. Mehta, Ohio University
FUZZY SETS ARE HELPFUL IN THE DIAGNOSIS OF CORONARY ARTERY STENOSIS
Krzysztof Cios
In the first part, we will discuss methods for defining fuzzy sets for the
recognition of coronary artery stenosis from post-exercise
planar thallium-201 scintigrams. Probability density functions
are used to define fuzzy sets. Machine learning algorithms
are used to generate diagnostic rules. These rules are then compared
for accuracy with rules specified by a nuclear cardiologist, using
sensitivities and specificities for each rule system.
We showed that we needed only 15 rules generated by the EXP machine
learning algorithm in order to perform as well as 68
cardiologist-specified rules.
The second part of the talk will describe a system in which fuzzy
generalized operators are aggregated with decision rules generated by the
machine learning algorithms EXP, CLILP2 and ALFS. Results obtained
for the three sets of decision rules yield, on average, an accuracy of
above 90%.
SEGMENTING BRAIN IMAGES WITH A FUZZY CLASSIFIER
Lawrence O. Hall
The segmentation of magnetic resonance images of the brain into
regions of interest is discussed. A fuzzy hybrid connectionist, s
ymbolic instance based learning algorithm is used. Fuzzy rules can
be generated after learning and the rules may provide information
about important inter- relationships among image features.
Continuous attributes are fuzzified in this approach to learning.
The generated fuzzy rules may be used in Fuzzy-CLIPS expert system
tool for classification. Segmentation results and issues are
discussed. The results show an excellent segmentation of a normal central
slice of the brain into gray matter, white matter and
cerebro spinal fluid.
FUZZY ANALYSES OF BIOLOGICAL INFORMATION PROCESSING
Safwan Shan
Neural information processing is mediated by complex
inter-relationships amongst cells. Existing methods
to carry out comprehensive evaluations of these
interactions are constrained by properties intrinsic
to neurobiological data: low mean cellular firing
rates and aperiodic sequences of firing times. We
have devised a technique to overcome these
difficlties. Using fuzzy logic to represent cellular
information processing characteristics,
we examined the functional dynamics of
multi-unit spiketrains. It was found that a fuzzy
analog of a neural spike train enhance the
opportunities to detect complex cellular
interactions and together with ANN's provides a
powerful environment withinn which to study neural
information processing.
A NEURO-FUZZY MEDICAL DECISION SUPPORT DEVELOPMENT WORKBENCH
Patrik Eklund
This work describes a laboratory and clinical support systems workbench,
DiagaiD, based on an efficient transfer of patient data between health care
professionals and clinical subsystems. The objectives of DiagaiD is to
provide a knowledge elicitation tool, and to support automatic generation of
stand-alone clinical decision support systems.
Domain experts can utilize the workbench in order to generate
rule bases and graphical user interfaces, and integrating them into
stand-alone systems. Thus the classical knowledge acquisition technique,
heavily depending on close cooperation between domain experts and
systems engineers, is replaced by fully automatic eliciation facilities.
DiagaiD has been installed in hospital use. The goal of this paper
is to demonstrate the power of the DiagaiD workbench.
FUZZY LOGIC CONTROL FOR RESTORING MOVEMENT TO PARAPLEGIC INDIVIDUALS
Howard Jay Chizeck
Limitations in the ability to stand, to negotiate curbing and stairs, and
to walk reduce the physical access of paraplegic individuals to certain
locations, restrict their employment opportunities and can lower their overall
quality of life. Existing functional neuromuscular stimulation (FNS) devices
for locomotion in paraplegic, stroke and head injury patients have involved
mostly open loop (pre-programmed) stimulation patterns, rather than feedback
control. This approach precludes correction for undesirable movements and
generally requires excess stimulation of muscles. Specific problems with
existing FNS systems include limited independent locomotion (i.e., an
assistant is needed when walking, for most subjects); variability of
motion; and a lack of compensation for fatigue and for changes in
slope and surface during FNS walking.
A novel fuzzy-logic based structure has been developed that uses
measurements obtained during one step to adjust the electrical stimulation
during subsequent steps of the gait cycle. The paraplegic subject receives
stimulation (of up to 48 channels) from the stimulator and implanted
electrodes.
Sensors measure physiological quantities during gait. A fuzzy logic based gait
event detector determines which phase of gait the subject is currently
performing. The rules for this gait event detector come from two sources. Some
rules are "hand crafted," are based common sense and experience of the
investigator. Other rules were derived using fuzzy
system identification methods, on the basis of sensor measurements and visual
observations. This information is used, along with the sensor measurements, by
a gait evaluator which processes gait phase information (from the detector) and
sensor measurements (angles, forces) to determine the "quality" of the gait
during each phase. A pattern adjuster then alters the stimulation, using fuzzy
logic. Sensor measurements used to detect gait events include: foot-floor
contact force and center-of-pressure (insole-mounted transducers); ankle angles
(brace-mounted potentiometers); and knee angles (skin-mounted angle sensors).
Gait anomalies, such as foot drag, and changes to the external environment
(such as walking surface slopes) can be accurately detected using this
approach, and necessary adjustment to the locomotion stimulation
pattern can be made. By improving the quality, repeatability and
predictability of FNS gait, this work is a contribution to improving
the practicality of FNS locomotion systems.
APPLICATION OF FUZZY ARTMAP FOR PREDICTION OF PROTEIN 3D STRUCTURE
Bhavin V. Mehta
Several techniques have been developed for protein secondary structure
prediction using different type of statistical and neural network techniques.
In this paper, the Fuzzy ARTmap paradigm and the Probabilistic Network
algorithm is used. The results of this new methods applied to
secondary structure prediction is compared with the existing
techniques like back error propagation method and the Chou-Fassman
statistical method.
Fuzzy ARTmap which has been selected in predicting the secondary
structure of proteins because of its architectural and neurodynamical
characteristics.is an incremental supervised learning algorithm which
combines fuzzy logic and adaptive resonance neural network for the
recognition of pattern categories and multidimensional maps in
response to input vectors presented in arbitrary order.
Its neurodynamics implements a new min-max learning rule which
conjointly minimizes predictive error and maximizes code compression,
and therefore generalization. In addition, Fuzzy ARTmap is easy to
use. It has small number of parameters, requires no problem specific
crafting or choice of initial weights, and does not get trapped in
local minima (specially with large data sets that we are using).
Fuzzy ARTmap has been implemented in a simulator developed in-house
using the C language.
The analysis of the results have demonstrated that the development
of a multi-expert architecture with different representation schemes
could be a better solution. This multi-expert architecture would
utilize rules based on statistical analysis and neural networks to grow with
examples. In addition, the utilization of additional information such as
energy levels is being considered.
USE OF APPROXIMATE REASONING IN A HYBRID EXPERT SYSTEM
FOR CHEST PAIN ANALYSIS
Donna Hudson
A decision support system for the analysis of chest pain
in the emergency room environment will be discussed and
demonstrated. The system consists of a rule-based
component which uses techniques from approximate reasoning
to combine partial presence of symptoms and weighted
antecedents to determine rule substantiation. This structure
permits fuzzy techniques to be incorporated into the decision
making process at two stages: in the development of the knowledge
base and in the direct use of the system by the end user.
The weighted antecedents provide a more complex rule structure
in which the expert is able to incorporate information pertaining
to the relative importance of each contributing parameter.
The partial presence of symptoms allows nuances in the data to be
recorded by the user more accurately than with a simple yes/no response.
In addition to the rule-based component, a neural network model is
included which can be used to determine membership functions and
rule thresholds, or can be used directly as a decision making tool.
This is a functioning system which will be demonstrated on an IBM-
compatible portable computer. Participants will have the
opportunity to try the system individually.
USE OF CHAOTIC ANALYSIS AS A MEANS OF QUANTIFYING LEVELS OF
VARIABILITY IN THE ANALYSIS OF HOLTER R-R INTERVALS
Maurice Cohen
Analysis of time series data is subject to problems
of nonlinear analysis, which can often lead to chaotic
states. A method for analyzing 24-hour Holter data will
be discussed. Analysis of these data sets in complicated
by the large number of points which are recorded in the
24 hour cycle, usually in excess of 100,000. Although
graphing procedures can provide pictorial evidence of the
amount of variability in these data, the physical plotting
procedure can take several hours using an automatic plotter
and often results in unreadable plots if all points are plotted
on the same graph. Through a continuous solution of the logistic
equation, a second-order difference plot has been developed which
demonstrates the degree to which these data points center around
the origin. Again, because of the large number of points, a
numerical measure of central tendency will be presented, which
gives a value between 0 and 1, with 1 indicating no variability.
These measures can be used to develop membership functions which
can be used in conjunction with fuzzy models in either knowledge-based
systems or neural networks.
NONINVASIVE DETECTION OF CORONARY ARTERY DISEASE USING
FUZZY MIN-MAX METHODS
Metin Akay
This study examines the use of fuzzy min-max method
for detecting coronary artery disease noninvasively by
extracting useful information from the diastolic heart sounds
associated with coronary occlusions. It has been widely reported that
coronary stenoses produce sounds due to the turbulent blood flow in
these vessels. These complex and highly attenuated signals taken from
recordings made in a soundproof room were detected and
analyzed to provide feature set. In addition, some physical
examination variables such as sex, age, body weight, smoking
condition, systolic and diastolic pressure were included in the feature vector.
