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PubMed1997

Self-learning fuzzy logic control of neuromuscular block.

We have assessed the performance of a "self-learning" fuzzy logic controller to administer atracurium to a required depth of neuromuscular block. We studied 20 ASA I and II patients undergoing surgery anticipated to last longer than 90 min. A Datex Relaxograph was used to measure the degree of neuromuscular block, and control to a T1 twitch height set point of 10% of baseline neuromuscular function was selected. The controller commenced with a blank rule-base and instructed a Graseby 3400 infusion pump to administer an atracurium infusion to maintain this level of block. The system achieved stable control of neuromuscular block with a mean T1 error of -0.52% (SD 0.55%) accommodating a range in mean atracurium infusion rate of 0.25-0.44 mg kg-1 h-1. These results compare favourably with the more computationally intensive and unwieldy adaptive control strategies for atracurium infusion used previously. There was less variation in infusion rates than in our previously studied fixed rules fuzzy controller.

Adult↗

PubMed2003

Survey of fuzzy logic applications in brain-related researches.

The aim of this study was to survey fuzzy logic (FL) applications in brain researches. In general, these applications are related to pattern recognition for localization in brain structures or tumor detection, image segmentation, and simulations. In recent years, neural networks and FL are gaining popularity. FL is based on the observation of people. The enormous amount of information representation by the brain suggests that FL principles can be useful, especially for complex brain functions. Causal models based on functional neuroanatomy can be then implemented in computer simulations to reflect the dynamical intersection of brain structures. FL is considered as an appropriate tool for modelling and control. FL has been applied in different ways to brain researches. This paper surveys the utilization of FL in brain researches.

Biomedical Research↗

PubMed2004

Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement.

In this paper, we describe a combined approach with fuzzy logic and structure tensor towards improved enhancement of possible MCs (microcalcifications) in digital mammograms. The proposed contrast enhancement algorithm has two operational components. One is structure tensor operator and the other is fuzzy enhancement operator, both of which are arranged in parallel to process the input digital mammograms. While the structure tensor operator processes the digital mammograms and produces a corresponding eigen-image to highlight the region-of-interests, the fuzzy enhancement operator fuzzifies the mammogram via the maximum fuzzy entropy principle in fuzzy domain. As a result, the local fuzzy contrast can be extracted and modified adaptively in accordance with the information provided by the eigen-image, and those non-MCs regions are suppressed by being taken as noise. After that, the mammogram is transformed back to the pixel domain and the enhanced mammogram is constructed. Extensive experimental results show that our proposed algorithm outperforms the existing benchmark in terms of cost figures across the whole range of true positive fractions (TPF).

Algorithms↗

PubMedDate not supplied

Adaptive fuzzy logic restriction rules for error correction and safe stimulation patterns during functional electrical stimulation.

Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules.

Electric Stimulation Therapy↗

PubMedDate not supplied

Fuzzy logic and medical device design controls.

The second article in this series concentrated on the application of fuzzy logic in drug delivery systems and presented a specific example of respiratory therapy. This article completes the picture with a discussion of the regulatory issues, with particular reference to hardware and software design controls and applicable standards.

Computer-Aided Design↗

PubMed2002

Fuzzy logic controller for weaning neonates from mechanical ventilation.

Weaning from mechanical ventilation is the gradual detachment from any ventilatory support till normal spontaneous breathing can be fully resumed. To date, we have developed a fuzzy logic controller for weaning COPD adults using pressure support ventilation (PS). However, adults and newborns differ in the pathophysiology of lung disease. We therefore used our fuzzy logic-based weaning platform to develop modularized components for weaning newborns with lung disease. Our controller uses the heart rate (HR), respiratory rate (RR), tidal volume (VT) and oxygen saturation (SaO2) and their trends deltaHR/deltat, deltaVT/deltat and deltaSaO2/deltat to evaluate, respectively, the Current and Trend weaning status of the newborn. Through appropriate fuzzification of these vital signs, Current and Trend weaning status can quantitatively determine the increase/decrease in the synchronized intermittent mandatory ventilation (SIMV) setting. The post-operative weaning courses of 10 newborns, 82+/-162 days old, were assessed at 2-hour intervals for 68+/-39 days. The SIMV levels, proposed by our algorithm, were matched to those levels actually applied. For 60% of the time both values coincided. For the remaining 40%, our algorithm suggested lower SIMV support than what was applied. The Area Under the Curve for integrated ventilatory support over time was 1203+/-846 for standard ventilatory strategies and 1152+/-802 for fuzzy controller. This suggests that the algorithm, approximates the actual weaning progression, and may advocate a more aggressive strategy. Moreover, the core of the fuzzy controller facilitates adaptation for body size and diversified disease patterns and sets the premises as an infant-weaning tool.

Area Under Curve↗

PubMed1993

[Diabetic retinopathy score and fuzzy logic in therapy].

A system for determining optimal treatment of diabetic retinopathy was devised on the basis of fuzzy logic. In a previous study we devised a new retinopathy scoring system from the multivariate analysis of fluorescein angiography findings. Using this scoring system we expressed severities as language terms and assigned them membership functions which showed grade (0-1). Next, the optimal treatment was determined by defuzzification of the membership functions given to treatment programs corresponding to the severity of retinopathy. This decision system is shown as a nomogram. Users of this system can get the grade for choosing optimal treatment after they input the retinopathy score into the nomogram. Assessment of this fuzzy decision system was performed by changes in the extent of dye leakage (Le) and neovascularization (NV) in 86 eyes of 48 diabetic patients followed up for over 12 months. When the grade of choice was over 0.5, the evaluation of choosing optimal treatment was 74% by Le and 68% by NV. Le improved but NV did not improve when the grade of choice was elevated to a higher level.

Aged↗

PubMed2006

Fuzzy-logic optical optimization of mainframe CPU and memory.

The allocation of CPU time and memory resources is a familiar problem in organizations with a large number of users and a single mainframe. Usually the amount of resources allocated to a single user is based on the user's own statistics not on the statistics of the entire organization, therefore patterns are not well identified and the allocation system is prodigal. A fuzzy-logic-based algorithm to optimize the CPU and memory distribution among users based on their history is suggested. The algorithm works on heavy and light users separately since they present different patterns to be observed. The result is a set of rules generated by the fuzzy-logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering of Tel Aviv University demonstrate the capabilities of the new algorithm.

Journal Article↗

PubMed2004

Fuzzy logic simulation of the testosterone effect on sex differences in Vandenberg-Kuse mental rotation scores.

The relationship between testosterone level and mental rotation was modeled using a fuzzy logical simulation. The fuzzy model of men's and women's testosterone levels and MR scores mapped the input "space" of testosterone to the output "space" of mental rotation. The simulation showed that a single variable (testosterone) can produce a significant difference in output. The model typifies sex difference in distribution of mental rotation scores previously observed experimentally.

Female↗

PubMed1992

[Chaos research, fractals, fuzzy logic. From stereotypes to reality--consequences for medicine].

Both language and conventional mathematics aim to describe reality. Although language is more flexible, it is usually also inaccurate, while mathematics permits accurate presentations and clear prognoses. However, it is burdened by the fact that it is not everywhere applicable. Such chaotic structures as clouds, or lang-term weather forecasting, for example, cannot be expressed in terms of mathematics. Chaos researchers are attempting, in a non-linear world, to understand mathematically dynamic, apparently unordered systems. In this connection, the fractal dimension also appears--which can be employed in the area of diagnosis to define tumor contours. Fuzzy logic comes closer to reality by replacing the inflexible yes/no by a more or less option and by introducing linguistic nuances into machine-controlled processes. Chaos research and fuzzy logic teach us that there is no such thing as certainty of action or prognosis. In the world as it is, whoever claims to possess it is either naive or guilty of self-deception.

Artificial Intelligence↗

PubMed1993

The paradigm and the fuzzy logical model of perception are alive and well.

Cutting, Bruno, Brady, and Moore (1992) criticized the paradigm for inquiry and the fuzzy logical model of perception (FLMP) presented in Massaro (1988a). In this reply to their remarks, it is shown that (a) the properties of the paradigm are ideal for inquiry; (b) models are best tested against the results of individual subjects and not average group data; (c) model fitting and analysis of variance do not give contradictory results; (d) the FLMP can be proven false and does not have a superpower to predict a plethora of functions or to absorb random variability; and (e) various extraneous characteristics of a model, such as equation length, cannot account for the success of the FLMP. On the other hand, the empirical findings of Cutting et al. give important new properties of pattern recognition. Finally, Cutting's theory of directed perception is compared with the FLMP.

Decision Making↗

PubMed2000

A fuzzy logic approach to analyzing gene expression data.

We have developed a novel algorithm for analyzing gene expression data. This algorithm uses fuzzy logic to transform expression values into qualitative descriptors that can be evaluated by using a set of heuristic rules. In our tests we designed a model to find triplets of activators, repressors, and targets in a yeast gene expression data set. For the conditions tested, the predictions made by the algorithm agree well with experimental data in the literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger number of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data.

Algorithms↗

PubMed1996

Neural networks and fuzzy logic in clinical laboratory computing with application to integrated monitoring.

We present an analysis of the computational features of neural networks and fuzzy logic architectures which attempts to explain their recent popularity as well as their drawbacks. Based upon many reports in several fields, we identify the key computational requirements in the clinical laboratory setting, and review several classical tools. In particular we make the observation that all of these needs may be viewed as a search for an appropriate mathematical mapping. We suggest that the neural networks promise as a universal function approximant is the main source of its apparent attractivity. We then describe a customized neural network architecture as a non-linear, adaptive signal processor for integrated monitoring. This architecture is employed in the Adaptive Real-Time Anesthesiologist Associate (ARTAA) system, which has been developed as a joint project at the Department of Anesthesiology, Albert Einstein Medical Center and the Electrical and Computer Engineering Department, Drexel University in Philadelphia, USA. In this application the neural network realizes a non-linear scalar map from the set of physiological signals to a vital function status (VFS) indicator. The system is now under clinical testing.

Anesthesia↗

PubMedDate not supplied

A new approach to the statistical treatment of 2D-maps in proteomics using fuzzy logic.

A new approach to the statistical treatment of 2D-maps has been developed. This method is based on the use of fuzzy logic and allows to take into consideration the typical low reproducibility of 2D-maps. In this approach the signal corresponding to the presence of proteins on the 2D-maps is substituted with probability functions, centred on the signal itself. The standard deviation of the bidimensional gaussian probability function employed to blur the signal allows to assign different uncertainties to the two electrophoretic dimensions. The effect of changing the standard deviation and the digitalisation resolution are investigated.

Electrophoresis↗

PubMed1989

Fuzzy logical approach to perception of dot numerosity.

The present study utilized a fuzzy logical approach for understanding human perception or judgments of dot numerosity. In Exp. 1 subjects were required to view dot patterns and to judge the truthfulness of the single and combined statements which asserted that the number of dots was large. The results indicated that (a) the rules based on the minimum and maximum truthfulness of the component statements best approximate subjective conjunction and disjunction about dot numerosity, when subjects kept the operations of the standard logic system in mind. (b) When the subjects based their judgments on perceptive impression, their judgments were best fitted by the multiplicative form.

Adult↗

PubMed1997

A new fuzzy logic based image enhancement.

In this paper a new fuzzifier (fh) and a new contrast intensification operator (NINT) have been proposed for the enhancement of computerized tomography (CT) images. The performance of the proposed technique has been compared with an existing fuzzy logic based enhancement technique in terms of a new fuzzy contrast measure. The superiority of the proposed technique has been established.

Fuzzy Logic↗

PubMed1996

The fuzzy logic of visuomotor control.

Biological sensorimotor control is characterized by the use of signals from large numbers of sensors, monitoring numerous variables. Among these are the exteroceptive signals from the eyes and ears. Many of the sensory signals are under efferent control, and the motor responses they evoke, whether at a simple reflex level or routed through the higher centres, appear to be task and context dependent. In technology the analysis and management of multiple-input, multiple-output systems clearly exceed the capabilities of classical servo control theory. In this commentary, new types of control system based on conditional logic are discussed in relation to the rules animals use to control movement. It is argued that the concepts of fuzzy logic control provide a useful and "biologically compatible" way of describing sensorimotor behaviour. An example is given of a robotic device under fuzzy control, in which behaviours are selected according to a visual assessment of motor task and context. Each behaviour is associated with a small subset of rules relating specific sensory variables to specific motor actions. The rule-based approach is also discussed in relation to neurophysiological theories regarding the interneuronal control of locomotion, including the recently adduced "parliamentary principle." The analysis and classification of behaviours and rules is seen as a useful preliminary to the future study of interneuronal systems.

Animals↗

PubMed2001

Modeling uncertainty in computerized guidelines using fuzzy logic.

Computerized Clinical Practice Guidelines (CPGs) improve quality of care by assisting physicians in their decision making. A number of problems emerges since patients with close characteristics are given contradictory recommendations. In this article, we propose to use fuzzy logic to model uncertainty due to the use of thresholds in CPGs. A fuzzy classification procedure has been developed that provides for each message of the CPG, a strength of recommendation that rates the appropriateness of the recommendation for the patient under consideration. This work is done in the context of a CPG for the diagnosis and the management of hypertension, published in 1997 by the French agency ANAES. A population of 82 patients with mild to moderate hypertension was selected and the results of the classification system were compared to whose given by a classical decision tree. Observed agreement is 86.6% and the variability of recommendations for patients with close characteristics is reduced.

Decision Making, Computer-Assisted↗