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PubMed2001

Prognosis of body fluid level by fuzzy logic technique.

Surgical fluid replacement is a critical issue in medicine as the fluid volume excess or deficit can both complicate the patient's condition. Currently, the administration of fluid volume is carried out primarily based on the experience and expertise of the anaesthetist as there is no analytical method available to estimate the patient's fluid level. The development of a decision support system (DSS) to assist the anaesthetist in estimating the required fluid infusion rate for a particular patient has been the focus of this research paper. The DSS is developed based on Fuzzy Logic Control (FLC) technique which is ideal for developing input/output models in an unstructured and/or complex environment. The fuzzy rules used in the DSS are derived automatically from the clinical data produced in surgical operations. The DSS employs a Multi Rule Base (MRB) learning scheme to adapt its model according to the significant variation in the physiological parameters of a patient. The performance of the developed algorithms is validated through experimental work using clinical data. The results obtained so far are encouraging.

Diagnosis, Computer-Assisted↗
PubMed2005

An intelligent control method based on fuzzy logic for a robotic testing system for the human spine.

In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine. An FLC is a real-time expert system that can emulate part of a human operator's knowledge by using a set of action rules. The FLC provides simple but robust solutions that cover a wide range of system parameters and can cope with significant disturbances. It can be viewed as a heuristic and modular way of defining a nonlinear, table-based control system. In this study, an FLC is developed which uses the force difference and the change in force difference as the input parameters, and the displacement as the output parameter. A rule-table based on these parameters is designed for the controller Experiments on a physical model composed of springs demonstrate the improved performance of the proposed method.

Algorithms↗
PubMed2006

Introduction of a new orthodontic treatment planning software; a fuzzy logic expert system.

General practitioners can learn the mechanical aspects of orthodontics quickly. But they are not usually sure about the treament plans they propose. In this article, new software is described that proposes treatment plans fornon-surgical orthodontic cases. This computer program can receive patients' data in both graphical and numerical forms. The concepts of fuzzy logic have been used to enable the software to work with nominal parameters. This feature greatly simplifies the use of the software because it does not force the user to measure several numerical variables. Each patients' data is saved by this software and the saved data can be modified or deleted later. The computer program is prepared to deal with special cases such as incomplete dentitions appropriately. The software is supposed to reduce the number of inappropriate extractions and erroneous treatment plans for orthodontic purposes.

Adult↗
PubMed2006

A simplified type-2 fuzzy logic controller for real-time control.

Increasingly, genetic algorithms (GAs) are used to optimize the parameters of fuzzy logic controllers (FLCs). Although GAs provide a systematic design approach, the optimization process is generally performed off-line using a plant model. Differences between the model and physical plant may result in unsatisfactory control performance when the FLCs are deployed in practice. Type-2 FLCs are an attractive alternative because they can better cope with modeling uncertainties. Unfortunately, type-2 FLCs are computationally intensive. This paper presents a simplified type-2 FLC that is suitable for real-time applications. The key idea is to only replace some critical type-1 fuzzy sets by type-2 sets. Experimental results indicate that the proposed simplified type-2 FLC is as robust as a conventional type-2 FLC, while lowering the computational cost.

Journal Article↗
PubMed2003

Applying value analysis and fuzzy logic to select areas for installing waste fills.

This article discusses a methodology for assessing and ranking a predefined universe of objects to assist in the selection of suitable areas for the construction of sanitary landfills. The methodology is, in principle, for universal application. Its development was initially based on the 'Value Analysis' methodology and then on the 'Fuzzy Eigenvector Method', or 'Fuzzy Logic' as it is sometimes referred to. The focus of the study herein is on the comparative appraisal of a group of elements which are part of the guidelines to be adopted when determining the choice of the most suitable site for a sanitary landfill. This set of guidelines includes technical, economical, environmental, social and political aspects. As an example of the methodology proposed, four different areas were chosen. They were studied to determine which is the most appropriate site for the development of a new solid waste fill site. The results for these four areas, together with the functions chosen and function weights, are presented here.

Decision Making↗
PubMed1999

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.

Algorithms↗
PubMed2003

Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic.

In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are drastically affected by a variety of artifacts and cardiovascular diseases, leading to inaccurate arterial blood pressure (ABP) measurement. The purpose of this paper is to improve the accuracy in the arterial pressure measurement by reducing interference in the OAs using a recursive weighted regression algorithm (RWRA). This method includes a fuzzy logic discriminator (FLD) and a recursive regression algorithm. The FLD is used to reduce the effect of artifacts caused by measurement motion disturbance or cardiovascular diseases, and to determine the truthfulness of the oscillation pulse. According to the truth degree, the relationship between the cuff pressure and OA is reconstructed using the regression algorithm. Because the regression method must utilize inverse matrix operation, which will be difficult to implement in an automatic or ambulatory monitor, the recursive regression method is proposed to solve this problem. To test the performance of this RWRA, 47 subjects underwent the ABP measurement using both the auscultation and the oscillometry combined with the RWRA. It was found that the average difference between the pooled blood pressures measured by the auscultation and those by the oscillometry combined with the RWRA was found to be only 4.9 mmHg. Clinical results demonstrated that the proposed RWRA is more robust than the traditional curve fitting algorithm (TCFA). We conclude that the proposed RWRA can be applied to effectively improve the accuracy of the oscillometric blood pressure measurement.

Adolescent↗
PubMed1989

Testing between the TRACE model and the fuzzy logical model of speech perception.

The TRACE model of speech perception (McClelland & Elman, 1986) is contrasted with a fuzzy logical model of perception (FLMP) (Oden & Massaro, 1978). The central question is how the models account for the influence of multiple sources of information on perceptual judgment. Although the two models can make somewhat similar predictions, the assumptions underlying the models are fundamentally different. The TRACE model is built around the concept of interactive activation, whereas the FLMP is structured in terms of the integration of independent sources of information. The models are tested against test results of an experiment involving the independent manipulation of bottom-up and top-down sources of information. Using a signal detection framework, sensitivity and bias measures of performance can be computed. The TRACE model predicts that top-down influences from the word level influence sensitivity at the phoneme level, whereas the FLMP does not. The empirical results of a study involving the influence of phonological context and segmental information on the perceptual recognition of a speech segment are best described without any assumed changes in sensitivity. To date, not only is a mechanism of interactive activation not necessary to describe speech perception, it is shown to be wrong when instantiated in the TRACE model.

Adult↗
PubMed2005

Optimization of fertirrigation efficiency in strawberry crops by application of fuzzy logic techniques.

A high level of price support has favoured intensive agriculture and an increasing use of fertilisers and pesticides. This has resulted in the pollution of water and soils and damage to certain eco-systems. The target relationship that must be established between agriculture and environment can be called "sustainable agriculture". In this work we aim at relating strawberry total yield with nitrate concentration in water at different soil depths. To achieve this objective, we have used the Predictive Fuzzy Rules Generator (PreFuRGe) tool, based on fuzzy logic and data mining, by means of which the dose that allows a balance between yield and environmental damage minimization can be determined. This determination is quite simple and is done directly from the obtained charts. This technique can be used in other types of crops permitting one to determine in a precise way at which depth the appropriate dose of nitrate fertilizer must be correctly applied, on the one hand providing the maximum yield but, on the other hand, with the minimum loss of nitrates that leachate through the saturated zone polluting aquifers.

Agriculture↗
PubMedDate not supplied

A fuzzy logic based apnoea monitor for SIDS risk infants.

A unit has been designed which monitors newborn infants at risk of Sudden Infant Death Syndrome (SIDS) in a home environment. The unit monitors respiration, electrocardiogram (ECG) and haemoglobin oxygen saturation (SpO2) in combination, in order to detect any potentially life threatening event at an early stage. Provision is made for the generation of both audible and silent alarms and for the storage of signals and other information before, during and after an alarm episode for diagnostic purposes. An intelligent fuzzy logic algorithm is used to process the signals monitored and to implement several propositions concerning their status in order to determine the probability of an apnoea event and initiate the appropriate action. This has substantially reduced the number of false alarms and of undetected dangerous situations compared with previous units, which greatly improves the reliability and usefulness of such a monitor.

Algorithms↗
PubMed2004

Fuzzy logic control of FES rowing exercise in paraplegia.

An indoor personal rowing machine (Concept 2 Inc., Morrisville, VT) has been modified for functional electrical stimulation assisted rowing exercise in paraplegia. To successfully perform the rowing maneuver, the voluntarily controlled upper body movements must be coordinated with the movements of the electrically stimulated paralyzed legs. To achieve such coordination, an automatic controller was developed that employs two levels of hierarchy. A high level finite state controller identifies the state or phase of the rowing motion and activates a low-level state-dedicated fuzzy logic controller (FLC) to deliver the electrical stimulation to the paralyzed leg muscles. A pilot study with participation of two paraplegic volunteers showed that FLC spent less muscle energy, and produced smoother rowing maneuvers than the existing On-Off constant-level stimulation controller.

Aged↗
PubMed2004

Fuzzy logic and causal reasoning with an 'n' of 1 for diagnosis and treatment of the stroke patient.

The current scientific model for clinical decision-making is founded on binary or Aristotelian logic, classical set theory and probability-based statistics. Evidence-based medicine has been established as the basis for clinical recommendations. There is a problem with this scientific model when the physician must diagnose and treat the individual patient. The problem is a paradox, which is that the scientific model of evidence-based medicine is based upon a hypothesis aimed at the group and therefore, any conclusions cannot be extrapolated but to a degree to the individual patient. This extrapolation is dependent upon the expertise of the physician. A fuzzy logic multivalued-based scientific model allows this expertise to be numerically represented and solves the clinical paradox of evidence-based medicine.

Evidence-Based Medicine↗
PubMed2000

A fuzzy logic model of fracture healing.

A quantitative biomechanical model describes the tissue transformation during healing of a transverse osteotomy of a sheep metatarsal. The model predicts bridging of the bone ends through cartilage, followed by the growth of a callus cuff, and finally, the resorption of callus after ossification of the interfragmentary gap. We suggest bone density or the modulus of elasticity do not sufficiently characterize healing tissue for predictive purposes. In addition to the stimulus reflected by strain energy density we introduce a new osteogenic factor based upon stress gradients and which predicts areas of a high osteogenic capacity. Our model distinguishes three basic types of tissue, namely bone, cartilage and fibrous tissue. A fuzzy controller is proposed to model the tissue reaction. A set of fuzzy rules derived from medical knowledge has been implemented to describe tissue transformation such as intramembraneous or chondral ossification, atrophy or destruction. Fuzzy logic is able to model tissue transformation processes within the numerical simulation of remodeling processes. This approach improves the simulation tools and affords the potential to optimize planning of animal experiments and conduct parametric studies.

Algorithms↗
PubMed2003

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers.

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than the statistical and artificial neural-network-based methods.

Aged↗
PubMed1997

Applying fuzzy logic to control cycling movement induced by functional electrical stimulation.

This study examines the design of a rational stimulation pattern for electrical stimulation and a robust closed-loop control scheme to improve cycling system efficacy for subjects with paraplegia. The stimulation patterns were designed by analyzing gravitation potential needed for the cycling movement of the lower limbs against a frictionless cycling ergometer and the response delay of electrically stimulated muscles. To simplify the cycling control system, the stimulation patterns were fixed and only the single gain of the stimulation patterns was adjusted via a feedback control algorithm. To circumvent the complexity involved with exactly modeling a stimulated muscle and cycling ergometer, a model-free fuzzy logic controller (FLC) was adopted herein for our control scheme. Comparison between FLC and conventional proportional-derivative (PD) controllers demonstrated that the FLC with asymmetrical membership function enabled the subject with paraplegia to maintain varied desired cycling speeds, particularly at lower cycling speed. By incorporating the rational stimulation patterns, the FLC can produce a smooth and prolonged cycling movement deemed necessary for designing various training protocols.

Biomedical Engineering↗
PubMed2000

Hemodynamic management of congestive heart failure by means of a multiple mode rule-based control system using fuzzy logic.

A rule-based system was designed to control the mean arterial pressure (MAP) and the cardiac output (CO) of a patient with congestive heart failure (CHF), using two vasoactive drugs: sodium nitroprusside (SNP) and dopamine (DPM). The controller has three different modes, that engage according to the hemodynamic state. The critical conditions control mode (CCC) determines the initial infusion rates, and continues active if the MAP or the CO fall outside of the defined criticality thresholds: an upper and a lower boundary for the MAP and a lower boundary for the CO. Inside the boundaries the control is performed by noncritical conditions control modes (NCC's), which are fuzzy logic controllers. If the CO is within normal range and the MAP is close to the goal range, then the MAP is driven using only SNP, in a single-input-single-output mode (NCC-SISO). Otherwise the NCC multiple-input-multiple-output is active (NCC-MIMO). The goal values for the controlled variables are defined as a band of 5 mmHg for the MAP and 5 mL/kg/min for the CO, but there is little concern for this application if the CO is too high (i.e., in practical terms the CO only needs to achieve a necessary minimum rate). The NCC-MIMO includes a gain adaptation algorithm to cope with the wide variety in sensitivities to SNP. Supervisory capabilities to ensure adequate drug delivery complete the controller scheme. After extensive testing and tuning on a CHF-hemodynamics nonlinear model, the control system was applied in dog experiments, which led to further enhancements. The results show an adequate control, presenting a fast response to setpoint changes with an acceptable overshoot.

Algorithms↗
PubMed2005

Trabecular bone fracture healing simulation with finite element analysis and fuzzy logic.

Trabecular bone fractures heal through intramembraneous ossification. This process differs from diaphyseal fracture healing in that the trabecular marrow provides a rich vascular supply to the healing bone, there is very little callus formation, woven bone forms directly without a cartilage intermediary, and the woven bone is remodelled to form trabecular bone. Previous studies have used numerical methods to simulate diaphyseal fracture healing or bone remodelling, however not trabecular fracture healing, which involves both tissue differentiation and trabecular formation. The objective of this study was to determine if intramembraneous bone formation and remodelling during trabecular bone fracture healing could be simulated using the same mechanobiological principles as those proposed for diaphyseal fracture healing. Using finite element analysis and the fuzzy logic for diaphyseal healing, the model simulated formation of woven bone in the fracture gap and subsequent remodelling of the bone to form trabecular bone. We also demonstrated that the trabecular structure is dependent on the applied loading conditions. A single model that can simulate bone healing and remodelling may prove to be a useful tool in predicting musculoskeletal tissue differentiation in different vascular and mechanical environments.

Animals↗
PubMed1998

A novel approach to microcalcification detection using fuzzy logic technique.

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach.

Breast Neoplasms↗