Sysmodelling Shon Assignment 4

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AUCKLAND UNIVERSITY OF TECHNOLOGY

Fuzzy Logic Approach in

Health CareAssignment 4

Shon U Johnny

Student ID : 0947283

Master Of Engineering Studies



Abst t

This Assi

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t deals with the appli¤

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of Ar tif i¤

ial Intelli

ence in medicine and health caresegments. This summar i¦ es the var ious fuzzy approaches taken. It explains about the changing face of

modern health care, application of ar tif icial intelligence in health care. It descr i bes the implementation

of intelligent alarms in Cardio Anaesthesia using fuzzy logic (Becker, H Kasmacher, Kalff, &

Zimmerman, 1994) and implementation of an automatic monitor ing and estimation tool for

cardiovascular system under ventr icular assistance using fuzzy reasoning. (Yoshizawa, Takeda,

Yambe, & Nitta, 1992). It also descr i bes the fuzzy logic Applications for Automatic control,

Supervision and Fault diagnosis (Iserman, 1998) It also evaluates a special ar ticle on anaesthetic

mishaps. (Gaba, Maxwell, & De Anda, 1987)



Introduction

Fuzzy logic is a form of multi-valued logic der ived from fuzzy set theory to deal with

reasoning that is approximate rather than precise. In contrast with "cr isp logic", where binary

sets have binary logic, the fuzzy logic var iables may have a membershi p value of not only 0

or 1 ± that is, the degree of truth of a statement can range between 0 and 1 and is not

constrained to the two truth values of classic propositional logic. Fur thermore, when

linguistic var iables are used, these degrees may be managed by specif ic functions.

A fuzzy set is a pair ( A,m) where A is a set and .

For each , m( x) is the grade of membershi p of x. If A = { x1,..., xn} the fuzzy set ( A,m)

can be denoted {m( x1) / x1,...,m( xn) / xn}.

An element mapping to the value 0 means that the member is not included in the fuzzy set, 1

descr i bes a fully included member. Values str ictly between 0 and 1 character ize the fuzzy

members. The set is called the support of the fuzzy set ( A,m) and

the set is called the kernel of the fuzzy set ( A,m).

Fuzzy Logic as Human Logic

In reality exact rules that cover the respective case perfectly can only be def ined for a few

distinct cases. These rules are discrete points in the continuum of possi ble cases and humans

approximate them. This approximation, and likewise the abstraction and think ing in

analogies, are only rendered possi ble by the f lexi bility of µhuman logic.¶ To implement this

human logic in engineer ing solutions, a mathematical model is required. Fuzzy logic has been

developed as such a mathematical model. Fuzzy logic can be viewed as an extension of

multi-valued logic. The three-valued logic of Lukasiewicz, containing µtruth,¶ µintermediate¶

and false values, is considered to be the basis of fuzzy logic. Fuzzy reasoning is in fact a

reasoning that is neither exact nor absolutely inexact, but only to a cer tain degree exact or

inexact. Unlike the reasoning based on classical logic, fuzzy reasoning aims at the modeling

of reasoning schemes based on uncer tain or imprecise information. In fuzzy logic, individual

elements can be members of a set to only a cer tain degree, that is, they can belong in different

degrees to different sets simultaneously.



R etr ieved October 27, 2009, from Electronics for you website:

(htt p://www.electronicsforu.com/electronicsforu/Ar ticles/ad.asp?ur l=/efylinux/efyhome/cove

r/additions/fuzzy.htm&title=Fuzzy%20Logic%20and%20its%20Advantages)



Problems being addressed by methods

presented

Technology-dr iven research, where medicine provides AI with a good set of problems is being used to develop techniques leading to more general- purpose technologies. These can

then be applied to other domains (for example, diagnosis of computer circuits). Inward-

look ing research, which addresses technical solutions that, although neither directly AI- based

nor of immediate concern to clinicians, are needed to suppor t the long-term goals of both

enterpr ises.

One such problem is the design and implementation of integrated electronic patient records.

Inside Problems

The apparent lack of progress in AI in Medicine has been attr i buted to separate challenges

faced by AIM (Ar tif icial Intelligence in Medicine) researchers and medical professionals.

AIM researchers attempt to solve diff icult computational problems like acquisition and

representation of a practitioner¶s knowledge and sk ills, integration of episodic longitudinal

and histor ical patient data and communication with real time monitors and devices.

Medical professionals must acquire and process knowledge. They are also subject to human

factor. The impact of diagnosis and treatment on patient¶s life, the threat of professional

liability and pressures of administrative and f inancial constraints are other issues that def ine

the viewpoint of a medical practitioner. These differences have led to conf licting or

contradictory goals in the par tnershi p between AIM researcher and their medical

collaborators. AI has also been hampered by its own concerns and limitations. Handling the

large quantities of well established medical knowledge, adapting to the dynamic and

uncer tain nature of medical practice and discover ing and modelling how physicians

successfully perform diagnosis are some problems signif icantly related to the medical

community.



Ex ernal Problems

Some external problems have affected AI¶s performance and use in clinical settings. Two of

the most commonly cited are lack of user acceptance and lack of user infrastructure, the f irst

being a two way problem. Physicians are usually concerned about the computer tak ing over

the decision mak ing without substantiating the suggested decisions and that are frequently

based on very limited knowledge .the systems usually work on a small subset of the patient

cases. The systems usually work on interfaces that interfere with workf low of processing and

treating patients. This concern is signif icant when the ser iousness of the decisions is high.

The practitioners are still accountable for the decisions made and not the AI systems. People

are more stereotypical towards the old AI systems even though the new systems have

replaced the old ones and are much more accurate and useful.

Fuzzy Approaches by Becker et al. and

Yoshizawa et al.

Now let us see the different fuzzy approaches taken in the papers provided. (Becker,

H Kasmacher, Kalff, & Zimmerman, 1994) and (Yoshizawa, Takeda, Yambe, & Nitta, 1992).

Dur ing open-hear t surgery, management of narcosis and stabilisation of the patient¶s

circulation are the most impor tant tasks of the anaesthetist. Currently, he is suppor ted by

monitor ing devices which present vital parameters like blood pressures, temperatures and

blood gases. Conventional monitor ing devices are equi pped with threshold alarm facilities

which means that if any parameter exceeds a predetermined, f ixed threshold, an acoustical

and optical alarm signal is generated .The limitation of this approach is the diff iculty of

determining appropr iate thresholds for each signal and the fact that deter ioration of onehemodynamic parameter like loss of blood volume can lead to a change of all blood pressures

i.e. ar ter ial pressure, venous pressure, atr ial pressure etc., each tr igger ing a different alarm

signal.



Fig: Conventional Alarm system

Fig: Fuzzy Alarm system

This approach is an intelligent alarm system to suppor t the anaesthetist in monitor ing the

patients hemodynamic state dur ing open-hear t surgery. Based on the decision mak ing process

of the anaesthetist, the most impor tant vital parameter constellations are evaluated using a

fuzzy inference approach. The evaluation yields an estimation of f ive hemodynamic statevar iables for which a continuous alarm visualisation is presented on the user interface.

Dur ing the complicated hear t surgery the intelligent alarm system gathers all information

from Anaesthesia Information System (AIS). For processing of exper t knowledge in the form

of diagnostic rules a fuzzy-inference approach is well suited.



K nowledge acquisi ion: Membership Func ions

Ten anaesthetists who have been work ing in cardio-aesthesia for between 3 and 15 years

individually rated their meaning on the bandwidth of terms like: very low, a little too low,

good, a little too high and very high of the measured vital parameters. For every term each

anaesthetist checked a par t of the measurement scale.

Each of the n (=lo) anaesthetists received a weight w = l/n (l/l0) and the weights were

summed up for each term. These f indings were used to def ine a set of linear and symmetr ic

membershi p functions for the linguistic input var iables which descr i be the vital parameters

hear t rate, systolic ar ter ial pressure lef t atr ial pressure and central venous pressure.

Fig: Membershi p function for linguistic var iable: Ar ter ial systolic pressure

Fuzzy k nowledge base:

A fuzzy knowledge base for the evaluation of the hemodynamic state var iables sets of

questionnaires was created. For each hemodynamic state var iable 25 stimuli were presented

graphically using vir tual analogue scales for the parameter representation in order to avoid

bias caused by linguistic uncer tainty. These stimuli covered the whole hemodynamic state

space. Thir teen exper ienced cardio anaesthetists marked their evaluation of the state var iable

for each stimulus on the given analogue scale. This estimation was transformed into fuzzy

rules about the state var iable



Fig: Extraction of the fuzzy rule weights.

Fif ty rules were acquired for each state var iable in this way. These rules were implemented in

the knowledge base of the intelligent alarm system.

User interface

The intelligent alarms are presented on the lef t section of the user interface in a prof ilogram.

It displays information about the state var iables related to the evaluation of the vital

parameter constellations.

Vital Trend Visualisation ± VTV

In the r ight section of the user interface shows the trend of the most impor tant hemodynamic

vital parameters, The VTV trajectory enables the anaesthetist to recognise pathologic trends

in the patient's circulatory functions and enables him to prevent deter ioration of the patient's

state.

Explanation module

This visualisation allows the anaesthetist to comprehend the system evaluation and to predict

the behaviour of the state var iable af ter blood volume or drug application by visuallyextrapolating the trend curve.



Automatic monitor ing and Estimation

tool for the cardiovascular System:

Now Let us see the Automatic monitor ing and Estimation tool for the cardiovascular System.

(Yoshizawa, Takeda, Yambe, & Nitta, 1992) This was done under the ventr icular assistance

using fuzzy reasoning. To promote clinical use of the lef t ventr icular assist device (LVAD),

the medical specialist should be kept free from constant attention to the operation or the

control behaviour of the LVAD. In this study a system to monitor the LVAD control system

is developed. It is named TOTOMES. It has a function that detects the presence and or igin of

the accidents happening in the cardiovascular system under the LVAD assistance. The fuzzy

reasoning algor ithms are used to detect the malfunctions and control the LVAD operations.

In order to avoid the problems like infection, dr if ting and poor durability, invasive

measurements for detecting circulatory abnormalities and malfunctions of LVAD control

system was made as little as possi ble. Here the directly measured data are Instantaneous dr ive

pressure PDR V (t), ejecting dr ive pressure level PP , f illing dr ive pressure level P N, outf low

rate from the LVAD f AH(t), aor tic pressure p(t), ECG signal, reference stock volume of the

LVAD sv*and reference systolic duration of the LVAD

*. The data are stored every 10 ms

into the personal computer system where the TOTOMES was implemented. Then the direct

measurements are processed and stored in secondary var iables.

These data are stored every 10 ms into a PC. The parameters of cardiovascular dynamics are

identif ied in a real time fashion by a time ser ies model. The instantaneous value of cardiac

out put of natural hear t is also identif ied on the basis of identif ied parameters.

Detection Procedures

The objects of detection are as follows:

Hardware

o Air tube of Pneumatic dr iver

o Cannulae connected with the patient



o Aor tic Pressure sensor

Sof tware

o R unning away of sof tware for pneumatic controller

o Divergence of adaptive control algor ithm

Cardiovascular system

o Hear t rate

o Cardiovascular parameters

o Cardiac Out put

Detection of malfunctions are realised by application of fuzzy logic to secondary var iables.



R easons for choosing Fuzzy Logic

Fuzzy Logic reduces the design development cycle

Fuzzy Logic simplif ies design complexity

With a fuzzy logic design methodology some time consuming steps are eliminated.

Moreover, dur ing the debugging and tuning cycle you can change your system by simply

modifying rules, instead of redesigning the controller. In addition, since fuzzy is rule based,

you do not need to be an exper t in a high or low level language which hel ps you focus more

on your application instead of programming. As a result, Fuzzy Logic substantially reduces

the overall development cycle.

Fuzzy Logic improves time to mark et

As we explained above, a fuzzy based design methodology addresses the time on design

complexity and marketing the end product.



A Better Alternative Solution to Non-Linear Control

A linear approximation technique is relatively simple, however it tends to limit control

performance and may be costly to implement in cer tain applications. A piecewise linear

technique works better, although it is tedious to implement because it of ten requires the

design of several linear controllers. A lookup table technique may hel p improve control performance, but it is diff icult to debug and tune. Fur thermore in complex systems where

multi ple inputs exist, a lookup table may be impractical or very costly to implement due to its

large memory requirements.

Fuzzy logic provides an alternative solution to non-linear control because it is closer to the

real wor ld. Non-linear ity is handled by rules, membershi p functions, and the inference

process which results in improved performance, simpler implementation, and reduced design

costs

Fuzzy Logic improves control perf ormance

With fuzzy logic we can use rules and membershi p functions to approximate any continuous

function to any degree of precision. We can also add more rules to increase the accuracy of

the approximation (similar to a Four ier transform), which yields an improved control

performance. R ules are much simpler to implement and much easier to debug and tune than

piecewise linear or lookup table techniques.

Fuzzy Logic simplif ies implementation

A linear approximation requires handling each input separately which multi plies designeffor t. Similar ly, a piecewise linear approach requires the design of several controllers and is

costly to implement. A lookup table seems more appropr iate for this problem but it takes time

to develop, debug and tune. For example, if we assume that each input requires eight bits, a

lookup table would require 64K entr ies which makes it very time consuming to implement.

Fuzzy Logic reduces hardware costs

Conventional techniques in most real life applications require complex mathematical analysis

and modelling, f loating point algor ithms, and complex branching. This typically yields a

substantial size of object code which requires a high end DSP chi p to run. Fuzzy Logic

enables you to use a simple rule based approach which offers signif icant cost savings, both in

memory and processor class.



Adaptive Fuzzy Techniques

Conventional Fuzzy logic systems like the PID control systems cannot adapt to gradual

changes in environments but only can adjust the behaviour from one execution to another, but

the rules themselves cannot change. These can only be used where the environments are

known and predictable.

By including adaptive control in Fuzzy logic we can implement design systems that can

adjust to environmental changes. Physical systems are subject to long term permanent

changes due to wear and tear to physical par ts and sensors. To compensate this process

designers incorporate fault tolerance in their PID models or they can add an ancilliary system

which confers adaptive nature to the controllers.

Adaptive Fuzzy Logic

An adaptive Fuzzy Logic system adjusts to time or process phased conditions and changes

the suppor ting system controls. Thus an adaptive system modif ies the character istics of the

rules. It resembles neural network in the way they work. An adaptive fuzzy system is highly

sophisticated and has a higher degree of adaptive parameters.

An adaptive fuzzy controller (Chr istine M, Michael A, & Dimitr is K)







BibliographyBecker, K., H Kasmacher, B., Kalff, R ., & Zimmerman, H.-J. (1994). A Fuzzy Logic Approach to

Intelligent Alarms in Cardioanesthesia. IEEE .

Chr istine M, H., Michael A, W., & Dimitr is K, P. Adaptive fuzzy controller that modif ies

membershi p functions.

Gaba, D., Maxwell, M., & De Anda, A. (1987). Anasthetic Mishaps: Break ing the chain of accident

Evolution. American Sociological Association.

Iserman, R . (1998). On fuzzy logic Applications for Automatic control, Supervision and Fault

diagnosis. IEEE .

Yoshizawa, M., Takeda, H., Yambe, T., & Nitta, S. -i. (1992). An Automatic Moniter ing and

Estimation tool for CardioVascular Assistance using Fuzzy reasoning. IEEE .

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