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PubMed1997

Self-organized marginal stability resulting from inconsistency between fuzzy logic and deterministic logic: an application to biological systems.

Complex systems in which internal agents (observers) interact with each other with finite velocity of information propagation cannot be described with a single consistent logic. We have proposed the bootstrapping system of cellular automata for describing such complex systems using two types of complementary logic: Boolean and non-Boolean. We extend this in this paper to a system of time-discrete continuous maps using fuzzy logic in place of non-Boolean logic. Fuzziness implies the intrinsic ambiguity of internal measurement. The bootstrapping system evolves, changing the dynamics perpetually, so that the discrepancy between the two types of complementary logic may be minimized. The equilibration force defined from the strength of discrepancy forms a landscape for self-organization which is similar to the fitness landscape for evolution. Though they appear similar, the former is derived from the internal dynamics. The goal of evolution, when applied to the map of the Belousov-Zabochinsky reaction, is demonstrated to be near the border between periodicity and chaos. The behavior depends on the degree of fuzziness and the extent of noise. When fuzziness increases too much, the system becomes unstable. Near the boundary, it exhibits intermittent chaos with a background of 1/f noise.

Animals↗
PubMed2001

Application of fuzzy logic in automated cow status monitoring.

Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level of detected cases of mastitis and estrus. Inputs for the fuzzy logic model were alerts from the detection model and additional information, such as the reproductive status. The output was a classification, true or false, of each alert. Only alerts that were classified true should be presented to the herd manager. Additional information was used to check whether deviating sensor measurements were caused by mastitis or estrus, or by other influences. A fuzzy logic model for the classification of mastitis alerts was tested on a data set from cows milked in an automatic milking system. All clinical cases without measurement errors were classified correctly. The number of false-positive alerts over time from a subset of 25 cows was reduced from 1266 to 64 by applying the fuzzy logic model. A fuzzy logic model for the classification of estrus alerts was tested on two data sets. The number of detected cases decreased slightly after classification, and the number of false-positive alerts decreased considerably. Classification by a fuzzy logic model proved to be very useful in increasing the applicability of automated cow status monitoring.

Animals↗
PubMed1996

Fuzzy logic control of inspired isoflurane and oxygen concentrations using minimal flow anaesthesia.

In order to evaluate the performance of feedback fuzzy logic control of inspired oxygen and isoflurane concentrations, we studied 30 patients undergoing discectomy for lumbar (n = 26) or cervical (n = 4) disc herniation. Patients were allocated random to one of two groups: a standard group (n = 15) with low flow anaesthesia (1.2-1.3 litre min-1) and manual control of gas concentrations; and a fuzzy group (n = 15) with minimal flow (0.5 litre min-1) and fuzzy logic feedback control of gas concentrations. Fuzzy logic control achieved and maintained very accurately the desired isoflurane concentration. Oxygen concentration was controlled more precisely than in the standard group. Delivery and costs of oxygen and nitrous oxide were significantly lower in the fuzzy group (P < 0.01). Accumulation of foreign gases was observed in one patient during low flow and in 11 patients during minimal flow anaesthesia. In conclusion, fuzzy logic control of inspired oxygen and isoflurane concentration during minimal flow anaesthesia was reliable and reduced anaesthetic gas delivery and costs.

Adult↗
PubMedDate not supplied

A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients.

This manuscript investigates different fuzzy logic controllers for the regulation of blood glucose level in diabetic patients. While fuzzy logic control is still intuitive and at a very early stage, it has already been implemented in many industrial plants and reported results are very promising. A fuzzy logic control (FLC) scheme was recently proposed for maintaining blood glucose level in diabetics within acceptable limits, and was shown to be more effective with better transient characteristics than conventional techniques. In fact, FLC is based on human expertise and on desired output characteristics, and hence does not require precise mathematical models. This observation makes fuzzy rule-based technique very suitable for biomedical systems where models are, in general, either very complicated or over-simplistic. Another attractive feature of fuzzy techniques is their insensitivity to system parameter variations, as numerical values of physiological parameters are often not precise and usually vary from patient to another. PI and PID controllers are very popular and are efficiently used in many industrial plants. Fuzzy PI and PID controllers behave in a similar fashion to those classical controllers with the obvious advantage that the controller parameters are time dependant on the range of the control variables and consequently, result in a better performance. In this manuscript, a fuzzy PI controller is designed using a simplified design scheme and then subjected to simulations of the two common diabetes disturbances--sudden glucose meal and system parameter variations. The performance of the proposed fuzzy PI controller is compared to that of the conventional PID and optimal techniques and is shown to be superior. Moreover, the proposed fuzzy PI controller is shown to be more effective than the previously proposed FLC, especially with respect to the overshoot and settling time.

Algorithms↗
PubMed2003

Weaning infants with respiratory syncytial virus from mechanical ventilation through a fuzzy-logic controller.

We have previously developed a fuzzy logic controller for weaning adults with chronic obstructive pulmonary disease using pressure support ventilation (PSV). We used the core of our fuzzy logic-based weaning platform and further developed parametrizable components for weaning newborns of differing body size and disease-state. The controller was validated on neonates recovering from congenital heart disease (CHD) while receiving synchronous intermittent mandatory ventilation (SIMV). We wished to compare the efficacy of this controller versus the bedside weaning protocol in children with respiratory syncytial virus pneumonitis/bronchiolitis (RSV) in the pediatric intensive care unit (PICU). The fuzzy controller evaluated the "current" and "trend" weaning status of the newborn to quantitatively determine the change in the SIMV integrated ventilatory setting. For the "current" status it used heart rate (HR), respiratory rate (RR), tidal volume (VT) and oxygen saturation (SaO2), while for the "trend" status the differences of deltaRR/ deltat, deltaHR/ deltat, and deltaSaO2/ deltat recorded between two subsequent time points were utilized. The enumerated vital signs were fuzzified and then probability levels of occurrence were assigned. Individualized "golden" goals for SaO2 were set for each newborn. We retrospectively assessed the charts of 19 newborns, 113+/-128 days old, 5,546+/-2,321 gr body weight, weaning for 99+/-46 days, at 2-hour intervals. The SIMV levels proposed by the fuzzy controller were matched to those levels actually applied. In 60% of the time both values coincided. For the remaining 40%, the controller was more aggressive suggesting lower values of SIMV than the applied ones. The Area under the SIMV curves over time was 1,969+/-1,044 for the applied vs 1,886+/-978 for the suggested levels, respectively. The fuzzy controller adjusted for body size and disease-pattern can approximate the actual weaning course of newborns with RSV.

Female↗
PubMed1994

Fuzzy-logic control of blood pressure through enflurane anesthesia.

OBJECTIVE: The objective of our study was to construct a closed-loop blood pressure control system using fuzzy logic during enflurane anesthesia. METHODS: Direct systolic blood pressure (SBP), the input variable, was assessed by a special fuzzy-logic membership function--that is, a triangulate continuum of grades between 0 and 1. We also set up the output membership function for the inhaled enflurane concentration. Four fuzzy-rule maps, or matrices, which determined the relationship between the changes of input variables and output values, were constructed based on published anesthetic values. The first map was based on the end-tidal anesthetic concentration known to block an adrenergic response. The fourth map was derived from the anesthetic effective dose (AD95). Fuzzy inference, arrived at by using fuzzy logic, followed the minimum-maximum center of gravity method. Anesthetic control started with the first map and was maintained with the succeeding maps. RESULTS: During anesthesia, the SBP remained within +/- 20% of the preanesthetic SBPs in 82% of the fuzzy control cases and within 83% during manual control. The difference was not significant. CONCLUSION: The anesthetist's management of the administration of the inhaled anesthetic enflurane was imitated by fuzzy-logic control of the blood pressure.

Adult↗
PubMed1996

Fuzzy logic control of mechanical ventilation during anaesthesia.

We have examined a new approach, using fuzzy logic, to the closed-loop feedback control of mechanical ventilation during general anaesthesia. This control system automatically adjusts ventilatory frequency (f) and tidal volume (VT) in order to achieve and maintain the end-tidal carbon dioxide fraction (FE'CO2) at a desired level (set-point). The controller attempts to minimize the deviation of both f and VT per kg body weight from 10 bpm and 10 ml kg-1, respectively, and to maintain the plateau airway pressure within suitable limits. In 30 patients, undergoing various surgical procedures, the fuzzy control mode was compared with human ventilation control. For a set-point of FE'CO2 = 4.5 vol% and during measurement periods of 20 min, accuracy, stability and breathing pattern did not differ significantly between fuzzy logic and manual ventilation control. After step-changes in the set-point of FE'CO2 from 4.5 to 5.5 vol% and vice versa, overshoot and rise time did not differ significantly between the two control modes. We conclude that to achieve and maintain a desired FE'CO2 during routine anaesthesia, fuzzy logic feedback control of mechanical ventilation is a reliable and safe mode of control.

Adolescent↗
PubMed1995

Fuzzy logic in drug delivery.

The first article in this series concentrated on the definition of terms and the methods that are applied in fuzzy logic. This article discusses the advantages of fuzzy logic in drug-delivery applications. The derivation of membership functions, linguistic variables, and drug-delivery control rules are shown for various delivery devices. A specific solution is discussed, which uses fuzzy logic decision-support methods for optimized treatment through respiratory drug delivery.

Administration, Inhalation↗
PubMed1997

Application of fuzzy logic in multicomponent analysis by optodes.

Fuzzy logic can be a useful tool for the determination of substrate concentrations applying optode arrays in combination with flow injection analysis, UV-VIS spectroscopy and kinetics. The transient diffuse reflectance spectra in the visible wavelength region from four optodes were evaluated to carry out the simultaneous determination of artificial mixtures of ampicillin and penicillin. The discrimination of the samples was achieved by changing the composition of the receptor gel and working pH. Different algorithms of pre-processing were applied on the data to reduce the spectral information to a few analytic-specific variables. These variables were used to develop the fuzzy model. After calibration the model was validated by an independent test data set.

Ampicillin↗
PubMed2006

The 0/0 problem in the fuzzy-logical model of perception.

The "Fuzzy-Logical Model of Perception" (FLMP) has often been questioned for its presumed ability to fit any data, but no clear-cut evidence has been presented yet. This paper demonstrates the ability of the FLMP to fit random data in the "McGurk region," that is in conditions involving conflicting stimuli. This is due to the so-called "0/0 problem," consisting in the fact that any audio-visual response can be fitted by the FLMP if the audio and visual stimuli provide at least one null probability in each possible category. The consequence is a high instability of the root mean square error in the region of the best fit.

Acoustic Stimulation↗
PubMed1997

A key for freshwater invertebrates using fuzzy logic.

MOTIVATION: This paper summarizes a biological key using a fuzzy logic expert system. This approach allows the user to make some errors, be uncertain about some answers and not answer questions if unsure, but have an opportunity to identify unknown specimens correctly. This approach was adopted in order to develop a 'forgiving' key for users with little or no prior knowledge of identification. The system was implemented using a multi-media expert system builder, 'Matcher', operating in a Microsoft Windows environment. RESULTS: The paper reports the design issues for a key to identify freshwater invertebrates to family level and describes the types of questions used within the key, the fuzzy inference engine, the structure of the system and the methodology adopted for testing it. Following this, an empirical test is reported of the hypothesis that a fuzzy logic expert system can improve identification accuracy for a sample of intended users. Finally, the discussion considers the practicalities of using such a system as an aid to organism identification.

Algorithms↗
PubMed2000

Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block.

OBJECTIVE: The important task for anaesthetists is to provide an adequate degree of neuromuscular block during surgical operations, so that it should not be difficult to antagonize at the end of surgery. Therefore, this study examined the application of a simple technique (i.e., fuzzy logic) to an almost ideal muscle relaxant (i.e., rocuronium) at general anaesthesia in order to control the system more easily, efficiently, intelligently and safely during an operation. METHODS: The characteristics of neuromuscular blockade induced by rocuronium were studied in 10 ASA I or II adult patients anaesthetized with inhalational (i.e., isoflurane) anaesthesia. A Datex Relaxograph was used to monitor neuromuscular block. And, ulnar nerve was stimulated supramaximally with repeated train-of-four via surface electrodes at the wrist. Initially a notebook personal computer was linked to a Datex Relaxograph to monitor electromyogram (EMG) signals which had been pruned by a three-level hierarchical structure of filters in order to design a controller for administering muscle relaxants. Furthermore, a four-level hierarchical fuzzy logic controller using the fuzzy logic and rule of thumb concept has been incorporated into the system. The Student's test was used to compare the variance between the groups. p < 0.05 was considered significant. RESULTS: The system achieved stable control of muscle relaxation with a mean T1% error of -0.19 (SD 0.66) % accommodating a range in mean infusion rate (MIR) of 0.21-0.49 mg x kg(-1) x h(-1). When these results were compared with our previous ones using the same hierarchical structure applied to mivacurium, less variation in the T1% error (p < 0.05) but the same variation in infusion rate were observed. The controller activity of these two drugs showed no significant difference (p > 0.5). However, the consistent medium coefficient variance (CV) of the MIR of both rocuronium (i.e., 36.13 (SD 9.35) %) and mivacurium (i.e., 34.03 (SD 10.76) %) indicated a good controller activity. CONCLUSIONS: The results showed that a hierarchical rule-based monitoring and fuzzy logic control architecture can provide stable control of neuromuscular block despite the considerable individual variation in neuromuscular block required among patients. Also, there was less variation in T1% error compared with that of previous study on mivacurium. Meanwhile, the consistent medium CV of the MIR of both rocuronium and mivacurium indicated a good controller activity which is able to withstand noise, diathermy effect, artifacts and surgical disturbances.

Adolescent↗
PubMed2000

Tests of auditory-visual integration efficiency within the framework of the fuzzy logical model of perception.

The fuzzy logical model of perception [FLMP, Massaro, Perceiving Talking Faces: From Speech Perception to a Behavioral Principle (MIT Press, Cambridge, MA, 1998)] has been extremely successful at describing performance across a wide range of ecological domains as well as for a broad spectrum of individuals. Because the model predicts optimal or maximally efficient integration, an important issue is whether this is the case for most individuals. Three databases are evaluated to determine to what extent a significant quantitative improvement in predictive ability can be obtained if integration is assumed to be somewhat inefficient. For the most part, there were no significant signs of inefficient integration. The previous differences found by Grant and Seitz [J. Acoust. Soc. Am. 104, 2438-2450 (1998)] must be due to their measures of efficiency, which appear to be invalid and/or conflate information with integration efficiency. Finally, the descriptive ability of the FLMP is shown to be theoretically informative and not simply the model's ability to describe any possible outcome.

Humans↗
PubMed2002

Pain model and fuzzy logic patient-controlled analgesia in shock-wave lithotripsy.

Pain control in conscious patients was investigated using a push-button, demand-driven supply of drugs. A fuzzy logic patient-controlled analgesia (PCA) algorithm was compared with a conventional algorithm, for alfentanil administration in extracorporeal shock-wave lithotripsy. The conventional PCA algorithm used an initial dose of 0.25mg, a fixed infusion rate of 60 mg h(-1) and a fixed bolus size of 0.2 mg with a 1 min lockout. The fuzzy logic PCA algorithm used an initial dose of 0.25 mg, a changeable infusion rate and a bolus size of 0.1 or 0.05 mg. The infusion rate was adjusted according to a look-up table that accepted the button-pressing history over the last three lockout intervals. The look-up table was designed using fuzzy logic. The bolus size was adjusted according to the button-pressing history over the past two lockout intervals. Twelve patients were treated using conventional PCA, and thirteen were treated with PCA + fuzzy logic control (FLC). PCA + FLC patients consumed 45% less drug. Also, PCA + FLC patients had a mean delivery/demand ratio of 82%, compared with 60% in conventional PCA. When the pain intensity scale was analysed, PCA + FLC patients had acceptable pain intensity at 62%, compared with 44% in conventional PCA.

Adult↗
PubMed2003

MRI definition of target volumes using fuzzy logic method for three-dimensional conformal radiation therapy.

PURPOSE: Three-dimensional (3D) volume determination is one of the most important problems in conformal radiation therapy. Techniques of volume determination from tomographic medical imaging are usually based on two-dimensional (2D) contour definition with the result dependent on the segmentation method used, as well as on the user's manual procedure. The goal of this work is to describe and evaluate a new method that reduces the inaccuracies generally observed in the 2D contour definition and 3D volume reconstruction process. METHODS AND MATERIALS: This new method has been developed by integrating the fuzziness in the 3D volume definition. It first defines semiautomatically a minimal 2D contour on each slice that definitely contains the volume and a maximal 2D contour that definitely does not contain the volume. The fuzziness region in between is processed using possibility functions in possibility theory. A volume of voxels, including the membership degree to the target volume, is then created on each slice axis, taking into account the slice position and slice profile. A resulting fuzzy volume is obtained after data fusion between multiorientation slices. Different studies have been designed to evaluate and compare this new method of target volume reconstruction and a classical reconstruction method. First, target definition accuracy and robustness were studied on phantom targets. Second, intra- and interobserver variations were studied on radiosurgery clinical cases. RESULTS: The absolute volume errors are less than or equal to 1.5% for phantom volumes calculated by the fuzzy logic method, whereas the values obtained with the classical method are much larger than the actual volumes (absolute volume errors up to 72%). With increasing MRI slice thickness (1 mm to 8 mm), the phantom volumes calculated by the classical method are increasing exponentially with a maximum absolute error up to 300%. In contrast, the absolute volume errors are less than 12% for phantom volumes calculated by the fuzzy logic method. On radiosurgery clinical cases, target volumes defined by the fuzzy logic method are about half of the size of volumes defined by the classical method. Also, intra- and interobserver variations slightly decrease with the fuzzy logic method, resulting in the definition of a better common volume fraction. CONCLUSION: Our fuzzy logic method shows accurate, robust, and reproducible results on phantoms and clinical targets imaged on MRI.

Algorithms↗
PubMed1997

Development of a fuzzy logic based system to monitor the electrical responses of nerve fiber.

A fuzzy logic based control system to monitor and control the electrical and chemical responses of nerve fibers has been designed and simulated. Fuzzy logic provides powerful reasoning mechanisms for combining information from multiple fuzzy sets.... The rule base has been developed based on the difference between the measured value and the desired value. The potential difference across each nerve fiber and the current through it has been monitored and controlled by a feedback control signal. The controller has been tested and simulated using sinusoidal and square wave forms with different frequency. The desired value was reached very rapidly. The system is capable of monitoring and controlling four nerve fibers simultaneously and allow the researcher to select different modes of operations.

Electrophysiology↗
PubMed1999

Automatic control of pressure support mechanical ventilation using fuzzy logic.

There is currently no universally accepted approach to weaning patients from mechanical ventilation, but there is clearly a feeling within the medical community that it may be possible to formulate the weaning process algorithmically in some manner. Fuzzy logic seems suited this task because of the way it so naturally represents the subjective human notions employed in much of medical decision-making. The purpose of the present study was to develop a fuzzy logic algorithm for controlling pressure support ventilation in patients in the intensive care unit, utilizing measurements of heart rate, tidal volume, breathing frequency, and arterial oxygen saturation. In this report we describe the fuzzy logic algorithm, and demonstrate its use retrospectively in 13 patients with severe chronic obstructive pulmonary disease, by comparing the decisions made by the algorithm with what actually transpired. The fuzzy logic recommendations agreed with the status quo to within 2 cm H(2)O an average of 76% of the time, and to within 4 cm H(2)O an average of 88% of the time (although in most of these instances no medical decisions were taken as to whether or not to change the level of ventilatory support). We also compared the predictions of our algorithm with those cases in which changes in pressure support level were actually made by an attending physician, and found that the physicians tended to reduce the support level somewhat more aggressively than the algorithm did. We conclude that our fuzzy algorithm has the potential to control the level of pressure support ventilation from ongoing measurements of a patient's vital signs.

Adult↗
PubMedDate not supplied

[Video-electroencephalography prolonged monitoring in patients with ambulatory diagnosis of medically refractory temporal lobe epilepsy: application of fuzzy logic's model].

INTRODUCTION: The video-electroencephalography (video-EEG) prolonged monitoring is an important auxiliary diagnostic instrument in epilepsy, and provides valuable information to classify the type of crisis and epileptic syndromes and to localize the epileptogenic zone. The fuzzy logic gives an efficient and intelligent analysis method, able to make inferences over ambiguous systems, and has been increasing used in several areas. AIMS: To correlate clinical and electroencephalographic data obtained with video-EEG of patients with clinical and interictal electroencephalography criteria of medically refractory temporal lobe epilepsy (TLE) and to make inferences of diagnosis precision with the application of the Fuzzy Hierarchy COPPE/Cosenza Model; to investigate the frequency in which the clinical, syndromic and topographic diagnosis can be modified in these patients; to evaluate the usefulness and applicability of fuzzy logic on the analysis of this type of study. PATIENTS AND METHODS: Transversal, prospective study, that included 22 adult outpatients with ambulatory diagnosis of medically refractory TLE, submitted to video-EEG monitoring that varied from 48 hours to 10 days. The grade of diagnosis precision was infered by fuzzy logic. RESULTS: The clinical diagnosis of epilepsy was modified in two (9%) patients, the syndromic in six (27.2%) and the topographic in 16 (72.7%). The fuzzy indicators of ambulatory diagnostic precision were: 0.91 to clinical diagnosis; 0.74 to syndromic diagnosis and 0.36 to topographic diagnosis, considering 1 the maximal precision diagnostic value (attributed to the video-EEG results). CONCLUSIONS: The video-EEG prolonged monitoring established the clinical, syndromic and topographic correct diagnosis in these patients; the fuzzy logic best defined a mathematical value of the correspondence between ambulatorial diagnosis of TLE and the video-EEG prolonged monitoring.

Adult↗