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PubMedDate not supplied

Using fuzzy sets to analyze putative correlates between age, blood type, gender and/or race with bacterial infection.

Previous studies have suggested that the demographic variables of age and blood type may serve as "risk factors" for infection by specific bacterial species. Since both demographic variables and bacterial species are defined using generally accepted parameters, they constitute highly suitable variables for the generation of a fuzzy logic program. A prospective study was therefore undertaken to examine the influence of age, blood type, gender and race on bacterial infection rates using a real database generated from 187 bacteremic patients admitted to Albert Einstein Medical Center. A fuzzy logic program was created using 155 randomly selected patients' data with four input (demographic variables) and four output classes (infections with "staphylococci", "streptococci", "Escherichia coli" or "non-E. coli gram negative rods (non-E.coli GNR)"). To see whether bacterial infection could be predicted based on demographic data alone, the program was tested using the remaining 32 patients' data. The program was able to correctly determine the bacterial output group of 27 of 32 randomly selected patients, giving an overall correlation of 84.38%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a powerful diagnostic tool for all physicians.

Adult↗
PubMed1995

Virtual reality in surgical education.

Virtual reality (VR) is an emerging technology that can teach surgeons new procedures and can determine their level of competence before they operate on patients. Also VR allows the trainee to return to the same procedure or task several times later as a refresher course. Laparoscopic surgery is a new operative technique which requires the surgeon to observe the operation on a video-monitor and requires the acquisition of new skills. VR simulation could duplicate the operative field and thereby enhance training and reduce the need for expensive animal training models. Our preliminary experience has shown that we have the technology to model tissues and laparoscopic instruments and to develop in real time a VR learning environment for surgeons. Another basic need is to measure competence. Surgical training is an apprenticeship requiring close supervision and 5-7 years of training. Technical competence is judged by the mentor and has always been subjective. If VR surgical simulators are to play an important role in the future, quantitative measurement of competence would have to be part of the system. Because surgical competence is "vague" and is characterized by such terms as "too long, too short" or "too close, too far," it is possible that the principles of fuzzy logic could be used to measure competence in a VR surgical simulator. Because a surgical procedure consists of a series of tasks and each task is a series of steps, we will plan to create two important tasks in a VR simulator and validate their use. These tasks consist of laparoscopic knot tying and laparoscopic suturing. Our hypothesis is that VR in combination with fuzzy logic can educate surgeons and determine when they are competent to perform these procedures on patients.

Clinical Competence↗
PubMed2003

The application of fuzzy-based methods to central nerve fiber imaging.

This paper discusses the potential of fuzzy logic methods within medical imaging. Technical advances have produced imaging techniques that can visualize structures and their functions in the living human body. The interpretation of these images plays a prominent role in diagnostic and therapeutic decisions, so physicians must deal with a variety of image processing methods and their applications. This paper describes three different sources of medical imagery that allow the visualization of nerve fibers in the human brain: (1) an algorithm for automatic segmentation of some parts of the thalamus in magnetic resonance images based on the differences in myelin content in various thalamic subnuclei; (2) polarized light for classifying the 3D orientation of the nerve fibers at each point; and (3) confocal laser scanning microscopy (CLSM) for calculating semiquantitative variables for myelin content. Fuzzy logic methods were applied to analyze these pictures from low- to high-level image processing. The solutions presented here are motivated by problems of routine neuroanatomic research demonstrating fuzzy-based methods to be valuable tools in medical image processing.

Algorithms↗
PubMed2005

Automatic fuzzy classification of the washout curves from magnetic resonance first-pass perfusion imaging after myocardial infarction.

OBJECTIVES: We sought to investigate the diagnostic ability of cardiac magnetic resonance imaging (MRI) perfusion in acute reperfused myocardial infarction. The study used fuzzy logic to automatically classify signal intensity-time curves from myocardial segments into 3 categories: normal, hypointense, and Hyperintense. MATERIALS AND METHODS: Thirty-eight patients with myocardial infarction underwent short-axis cine-MRI and contrast-enhanced MRI to provide data on wall thickening and the transmural extent of infarction. Of these, 17 had a second cardiac MRI to ascertain the functional recovery in each segment. RESULTS: The fuzzy logic based classification performs well (kappa= 0.87, P < 0.01) in comparison with a visual approach. Segments providing "hypo" curves do not recover (Delta = 0.11 SD = 0.96) whereas segments demonstrating the other curve types recover (Delta = 1 SD = 1.98 for "hyper" curves and Delta = 1.54 SD = 1.77 for "normal" curves). CONCLUSIONS: The proposed automatic signal intensity-time curve classification has a prognostic value when studying the functional recovery of pathologic segments and clearly identifies the no-reflow phenomenon known to induce poor recovery.

Adult↗
PubMed2001

Modeling face identification processing in children and adults.

Two face identification experiments were carried out to study whether and how children (5-year-olds) and adults integrate single facial features to identify faces. Using the paradigm of the Fuzzy Logical Model of Perception each experiment used the same expanded factorial design, with three levels of eyes variations crossed with three levels of mouth variations as well as their corresponding half-face conditions. In Experiment 1, an integration of facial features was observed in adults only. But, in adjusting the salience of the features varied, the results of Experiment 2 indicate that children and adults evaluated and integrated information from both features to identify a face. A weighted Fuzzy Logical Model of Perception fit the judgments significantly better than a Single Channel Model and questions previous claims of holistic face processing. Although no developmental differences in the stage of the integration of facial information were observable, differences between children and adults appeared in the information used for face identification.

Adult↗
PubMed1995

[New approaches to computer-assisted diagnosis of rheumatologic diseases].

UNLABELLED: Since the 1960s, several knowledge-based systems for computer-assisted diagnosis in radiology have been developed. The great majority of these tools has been implemented as off-line systems. This requires interaction with the system solely for the purpose of consultation and therefore interrupts the radiologist's work flow. This and inadequate man-machine interfaces may have inhibited the routine clinical use of such systems. The goal of this paper is to describe the current research toward the development of the on-line expert system Cadiag-4/Rheuma-Radio. The underlying fundamentals of the system design, including client/server architecture, communication interfaces, and fuzzy set theory and fuzzy logic as methods for knowledge representation and interference, are presented. METHODS: In radiology today, computers are routinely used to acquire radiological images in hospital and radiology information systems (HIS/RIS) and picture archiving and communication systems (PACS). In our approach, we make use of pre-existent sources of information to build an expert system that minimizes the interaction between radiologists and the computer. To handle uncertainty and vagueness of medical knowledge, fuzzy set theory and fuzzy logic are used. Given data of a specific case, a deductive inference procedure combines the observed radiological signs, establishes confirmed and excluded diagnoses as well as diagnostic hypotheses, and provides explanations for these conclusions. Furthermore, proposals for confirmation or exclusion of diagnostic hypotheses are offered. RESULTS: For evaluation purposes, an early prototype of Cadiag-4/Rheuma-Radio was tested on radiological disorders of the hip joint related to rheumatological diseases. Twenty radiological cases were used as test cases, reaching a diagnostic accuracy of about 80%. CONCLUSION: The first results are acceptable and encourage further work to cover the whole area of rheumatologically relevant radiological signs and diagnoses. Furthermore, research into the development of user-oriented data acquisition tools will be carried out.

Arthritis, Rheumatoid↗
PubMed2006

Groundwater vulnerability assessment for organic compounds: fuzzy multi-criteria approach for Mexico city.

This study was based on a groundwater vulnerability assessment approach implemented for the Mexico City Metropolitan Area (MCMA). The approach is based on a fuzzy multi-criteria procedure integrated in a geographic information system. The approach combined the potential contaminant sources with the permeability of geological materials. Initially, contaminant sources were ranked by experts through the Analytic Hierarchy Process. An aggregated contaminant sources map layer was obtained through the simple additive weighting method, using a scalar multiplication of criteria weights and binary maps showing the location of each source. A permeability map layer was obtained through the reclassification of a geology map using the respective hydraulic conductivity values, followed by a linear normalization of these values against a compatible scale. A fuzzy logic procedure was then applied to transform and combine the two map layers, resulting in a groundwater vulnerability map layer of five classes: very low, low, moderate, high, and very high. Results provided a more coherent assessment of the policy-making priorities considered when discussing the vulnerability of groundwater to organic compounds. The very high and high vulnerability areas covered a relatively small area (71 km(2) or 1.5% of the total study area), allowing the identification of the more critical locations. The advantage of a fuzzy logic procedure is that it enables the best possible use to be made of the information available regarding groundwater vulnerability in the MCMA.

Cities↗
PubMed2005

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.

Algorithms↗
PubMed1999

Neurofuzzy adaptive controlling of selective stimulation for FES: a case study.

A controller was designed for the selective stimulation of the sciatic nerve with a multiple contact cuff electrode to generate a desired torque in the ankle joint of cat. The design integrates three approaches, artificial neural network (ANN) modeling, fuzzy logical adaptation, and geometrical mapping. The geometrical mapping refers to the vector transformation from the joint coordinates to the virtual muscle coordinates which have been conceptually developed to represent the major recruitment features of contact-based functional units in the physical plant. This method reduces the complexity of generating a data set for training the neural network in the feedforward path and implementing the on-line learning algorithm embedded in the feedback loop. The controller was evaluated by computer simulation with the experimental data obtained from the torque generation in five acute cats. The results show that the ANN-based feedforward is capable of predicting 65% of a given desired isometric torque, and the fuzzy logical machine is able to provide suitable gains for feedback modulation to reduce the error from 35 to 8.5% and produce a robust control.

Animals↗
PubMed2004

Integration across frequency bands for consonant identification.

A comparison of the predictions of models of integration to data on the reception of consonants filtered into a variety of frequency bands is reported. New data on the consonant identification are presented. Three experiments were conducted testing the following bands: experiment I, 0-2100 Hz and 2100-4500 Hz; experiment II, 0-700 Hz combined with 700-1400, 1400-2100, 2100-2800, and 2800-4500 Hz; experiment III, all combinations of 700-1400, 1400-2100, 2100-2800, and 2800-4500 Hz. The predictions of four models, Fletcher's [Speech and Hearing in Communication (Van Nostrand, New York, 1950)] independent errors model, Massaro's fuzzy logical model of perception [Proc. Int. Congress of Phonetic Sciences, Stockholm, Vol. 3, pp. 106-113 (1987)], and Braida's pre-labelling and post-labelling models of integration [Q. J. Exp. Psychol. A 43, 647-677 (1991)], were compared in terms of their ability to predict combined-band scores. At least two models were capable of predicting performance for each combined-band condition. For experiment I, all models were able to make satisfactory predictions. For experiment II, a variant of the pre-labelling model was able to make satisfactory predictions. For experiment III, no model was able to make satisfactory predictions, but the fuzzy logical model of perception and a variant of the pre-labelling model made relatively good predictions. Thus the ability of the models to predict performance depended more on whether the condition included the lowest frequency band than on the adjacency or frequency separation.

Adult↗
PubMed2004

Hardware implementation of fuzzy Petri net as a controller.

The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented.

Algorithms↗
PubMed1993

Application of multivariate, fuzzy set and neural network analysis in quantitative cytological examinations.

Multivariate statistical methods have been used in several studies to increase the diagnostic reliability of TV image analyser systems. In recent years some algorithms for decision support (fuzzy logic) and for pattern recognition (neural nets), both non-linear, were developed. This paper reports on preliminary results obtained with these methods in quantitative cytology and compares them to the traditional classifiers. A total of 21 normal, 15 dysplastic and 23 malignant, gastric imprint smears were Feulgen stained and analysed on a Leitz Miamed DNA cytophotometer system. Mean DNA content, the 2c deviation index (2cDI), 5c exceeding rate (5cER), G1, S, G2 phase fraction ratios, cell nucleus area and form factor were determined. Diagnostic accuracy of the discriminant analysis was 96% for the malignant cases, 87% for dysplasias and 81% for normal cases. Cluster analysis gave no significant result. Our diagnostic system utilizing fuzzy logic has made the diagnostic borders adjustable and reliable. The back-propagation neural net correctly classified the normal and malignant cases (100%) and all but one of the dysplasias (98%). The non-linear mathematical methods improved the reliability of the diagnostic system. These new algorithms gave results comparable to traditional classifiers. The application of these methods to clinical samples is encouraging.

Algorithms↗
PubMed1998

Computer simulation of FES standing up in paraplegia: a self-adaptive fuzzy controller with reinforcement learning.

Using computer simulation, the theoretical feasibility of functional electrical stimulation (FES) assisted standing up is demonstrated using a closed-loop self-adaptive fuzzy logic controller based on reinforcement machine learning (FLC-RL). The control goal was to minimize upper limb forces and the terminal velocity of the knee joint. The reinforcement learning (RL) technique was extended to multicontroller problems in continuous state and action spaces. The validated algorithms were used to synthesize FES controllers for the knee and hip joints in simulated paraplegic standing up. The FLC-RL controller was able to achieve the maneuver with only 22% of the upper limb force required to stand-up without FES and to simultaneously reduce the terminal velocity of the knee joint close to zero. The FLC-RL controller demonstrated, as expected, the closed loop fuzzy logic control and on-line self-adaptation capability of the RL was able to accommodate for simulated disturbances due to voluntary arm forces, FES induced muscle fatigue and anthropometric differences between individuals. A method of incorporating a priori heuristic rule based knowledge is described that could reduce the number of the learning trials required to establish a usable control strategy. We also discuss how such heuristics may also be incorporated into the initial FLC-RL controller to ensure safe operation from the onset.

Arm↗
PubMed1999

An expert system for monitor alarm integration.

OBJECTIVE: Intensive care and operating room monitors generate data that are not fully utilized. False alarms are so frequent that attending personnel tends to disconnect them. We developed an expert system that could select and validate alarms by integration of seven vital signs monitored on-line from cardiac surgical patients. METHODS: The system uses fuzzy logic and is able to work under incomplete or noisy information conditions. Patient status is inferred every 2 seconds from the analysis and integration of the variables and a unified alarm message is displayed on the screen. The proposed structure was implemented on a personal computer for simultaneous automatic surveillance of up to 9 patients. The system was compared with standard monitors (SpaceLabs PC2), using their default alarm settings. Twenty patients undergoing cardiac surgery were studied, while we ran our system and the standard monitor simultaneously. The number of alarms triggered by each system and their accuracy and relevance were compared. Two expert observers (one physician, one engineer) ascertained each alarm reported by each system as true or false. RESULTS: Seventy-five percent of the alarms reported by the standard monitors were false, while less than 1% of those reported by the expert system were false. Sensitivity of the standard monitors was 79% and sensitivity of the expert system was 92%. Positive predictive value was 31% for the standard monitors and 97% for the expert system. CONCLUSIONS: Integration of information from several sources improved the reliability of alarms and markedly decreased the frequency of false alarms. Fuzzy logic may become a powerful tool for integration of physiological data.

Blood Gas Analysis↗
PubMed2003

Modeling and control of the magnetic suspension system.

A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.

Computer Simulation↗
PubMed2002

A heuristic expert system for forest fire guidance in Greece.

Forests and forestlands are common inheritance for all Greeks and a piece of the national wealth that must be handed over to the next generations in the best possible condition. After 1974, Greece faces a severe forest fire problem and forest fire forecasting is the process that will enable the Greek ministry of Agriculture to reduce the destruction. This paper describes the basic design principles of an Expert System that performs forest fire forecasting (for the following fire season) and classification of the prefectures of Greece into forest fire risk zones. The Expert system handles uncertainty and uses heuristics in order to produce scenarios based on the presence or absence of various qualitative factors. The initial research focused on the construction of a mathematical model which attempted to describe the annual number of forest fires and burnt area in Greece based on historical data. However this has proven to be impossible using regression analysis and time series. A closer analysis of the fire data revealed that two qualitative factors dramatically affect the number of forest fires and the hectares of burnt areas annually. The first is political stability and national elections and the other is drought cycles. Heuristics were constructed that use political stability and drought cycles, to provide forest fire guidance. Fuzzy logic was applied to produce a fuzzy expected interval for each prefecture of Greece. A fuzzy expected interval is a narrow interval of values that best describes the situation in the country or a part of the country for a certain time period. A successful classification of the prefectures of Greece in forest fire risk zones was done by the system, by comparing the fuzzy expected intervals to each other. The system was tested for the years 1994 and 1995. The testing has clearly shown that the system can predict accurately, the number of forest fires for each prefecture for the following year. The average accuracy was as high as 85.25% for 1995 and 80.89% for 1994. This makes the Expert System a very important tool for forest fire prevention planning.

Conservation of Natural Resources↗
PubMed2001

Neuro-fuzzy extraction of angular information from muscle afferents for ankle control during standing in paraplegic subjects: an animal model.

This paper is part of a project whose aim is the implementation of closed-loop control of ankle angular position during functional electrical stimulation (FES) assisted standing in paraplegic subjects using natural sensory information. In this paper, a neural fuzzy (NF) model is implemented to extract angular position information from the electroneurographic signals recorded from muscle afferents using cuff electrodes in an animal model. The NF model, named dynamic nonsingleton fuzzy logic system is a Mamdani-like fuzzy system, implemented in the framework of recurrent neural networks. The fuzzification procedure implemented was the nonsingleton technique which has been shown in previous works to be able to take into account the uncertainty in the data. The proposed algorithm was tested in different situations and was able to predict reasonably well the ankle angular trajectories especially for small excursions (as during standing) and when the stimulation sites are far from the registration sites. This suggests it may be possible to use activity from muscle afferents recorded with cuff electrodes for FES closed-loop control of ankle position during quite standing.

Algorithms↗
PubMed2005

Fuzzy classification: towards evaluating performance on a surgical simulator.

Computer-based surgical simulators such as the MIST-VR are able to provide scoring metrics such as time taken to complete a task, number of errors made, and economy of movement. Using MIST-VR's basic metrics, we explored the possibility of classifying skill levels using fuzzy logic. Our objective was to create a fuzzy classifier capable of classifying the performance of a subject training on a surgical simulator into 1 of 3 categories: Novice, Intermediate, and Expert. To accomplish this, we needed to establish a baseline skill level for each category. We had four laparoscopic surgeons, four surgical assistants/residents and four non-surgical staff/students with no laparoscopic experience perform two basic tasks on the simulator involving the placement of a ball into a box. We have found, through this preliminary study, that the results were inconclusive. We suspected a number of issues such as the size of our sample space used to train our classifier, and the difficulty of the chosen tasks adversely affected our results.

Canada↗