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At least 649 records · Page 36Linked to original sources
PubMed1992

Fuzzy classification of hemodynamic trends and artifacts: experiments with the heart rate.

Fuzzy set theory allows one to map inexact data, concepts, and events to fuzzy sets via user-defined membership functions. This paper describes a method for (1) robustly estimating the mean and slope of an arbitrary number of data points, (2) developing a set of fuzzy membership functions to classify various properties of heart rate trends, and (3) finding the longest consecutive sequence of heart rate data that fit a particular fuzzy membership function. Preliminary results indicate that fuzzy set theory has significant potential in the development of a clinically robust method for classifying heart rate data, trends, and artifacts.

Data Interpretation, Statistical↗
PubMed1994

[Evaluation of nutrient intake using fuzzy sets].

Fuzzy sets are especially suitable to evaluate the intake of a nutrient. For the evaluation of several components, the harmonic mean of the individual fuzzy values proves to be the best compromise. The mean of all nutrients results in the so called Prerow value, which can be used for the evaluation of the nutrition status. Maximizing the Prerow value is suitable for optimizing nutrition.

Eating↗
PubMed2004

Classification of cardiac abnormalities using heart rate signals.

The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%.

Arrhythmias, Cardiac↗
PubMed2001

Rate-adaptive pacemaker controlled by motion and respiratory rate using neuro-fuzzy algorithm.

Rate-adaptive pacemakers use information from sensors to change the rate of heart stimulation. Until now, fuzzy-pacemaker algorithms have been used to combine inputs from sensors to improve heart rate control, but they have been difficult to implement. In this paper, a pacemaker algorithm which controlled heart rate adaptively by motion and respiratory rate was studied. After chronotropic assessment exercise protocol (CAEP) tests were performed to collect activity and respiratory rate signals, the intrinsic heart rate was inferred from these two signals by a neuro-fuzzy method. For 10 subjects the heart rate inference, using the neuro-fuzzy algorithm, gave 52.4% improved accuracy in comparison with the normal fuzzy table look-up method. The neuro-fuzzy method was applied to a real pacemaker by reduced mapping of the neuro-fuzzy look-up table.

Adaptation, Physiological↗
PubMed2005

Forecasting epilepsy from the heart rate signal.

Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30 s prior to seizure onset Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.

Algorithms↗
PubMed2002

Fuzzy rules to predict degree of malignancy in brain glioma.

The current pre-operative assessment of the degree of malignancy in brain glioma is based on magnetic resonance imaging (MRI) findings and clinical data. 280 cases were studied, of which 111 were high-grade malignancies and 169 were low-grade, so that regular and interpretable patterns of the relationships between glioma MRI features and the degree of malignancy could be acquired. However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on a fuzzy min-max neural network (FMMNN) was used. The performance of a multi-layer perceptron network (MLP) trained with the error back-propagation algorithm (BP), the decision tree algorithm ID3, nearest neighbour and the original fuzzy min-max neural network were also evaluated. The results showed that two fuzzy decision rules on only six features achieved an accuracy of 84.6% (89.9% for low-grade and 76.6% for high-grade cases). Investigations with the proposed algorithm revealed that age, mass effect, oedema, postcontrast enhancement, blood supply, calcification, haemorrhage and the signal intensity of the T1-weighted image were important diagnostic factors.

Brain Neoplasms↗
PubMedDate not supplied

A fuzzy pattern recognition method to classify esophageal motility records.

Analysis and classification of esophageal motility records were investigated using signal processing and fuzzy-set pattern recognition techniques. A set of parameters has been extracted from the raw records and has previously been used as characterizing features. Improvements to these procedures were obtained by extracting these features from processed data, and some additional parameters were developed. The new set of features was used in the design of a fuzzy classifier, and classification accuracy was estimated using the leave-one-out technique. To our knowledge, this is the first report of the application of automatic classification to esophageal motility records.

Data Collection↗
PubMed1995

Fuzzy classification of nucleotide sequences and bacterial evolution.

A new method for reconstructing evolutionary relationship among bacteria by use of rRNA sequence data is proposed. The method is based on the concept of fuzzy classification of probabilities p(i), p(i/j) and p(i/j*) (i = A, G, C, U) of each sequence. The resulting partition tree shares common features of previous works but has some new peculiarities.

Animals↗
PubMed1999

Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system.

The classification of heart beats is important for automated arrhythmia monitoring devices. The study describes two different classifiers for the identification of premature ventricular complexes (PVCs) in surface ECGs. A decision-tree algorithm based on inductive learning from a training set and a fuzzy rule-based classifier are explained in detail. Traditional features for the classification task are extracted by analysing the heart rate and morphology of the heart beats from a single lead. In addition, a novel set of features based on the use of a filter bank is presented. Filter banks allow for time-frequency-dependent signal processing with low computational effort. The performance of the classifiers is evaluated on the MIT-BIH database following the AAMI recommendations. The decision-tree algorithm has a gross sensitivity of 85.3% and a positive predictivity of 85.2%, whereas the gross sensitivity of the fuzzy rule-based system is 81.3%, and the positive predictivity is 80.6%.

Decision Trees↗
PubMed1994

Fuzzy and conventional control of high-frequency ventilation.

A high-frequency ventilator was developed, consisting of a single-phase induction motor, an unbalanced mass and a mechanical vibration system. Intermittent positive pressure respiration was combined with high-frequency ventilation to measure end-tidal pCO2. Hysteresis was observed between the rotational frequency of the high-frequency ventilator and end-tidal pCO2. A fuzzy proportional plus integral control system, designed on the basis of the static characteristics of the controlled system and a knowledge of respiratory physiology, successfully regulated end-tidal pCO2. The characteristics of gas exchange under high-frequency ventilation was approximated by a first-order linear model. A conventional PI control system, designed on the basis of the approximated model, regulated end-tidal pCO2 with a performance similar to that of the fuzzy PI control system. The design of the fuzzy control system required less knowledge about the controlled system than that of the conventional control system.

Animals↗
PubMedDate not supplied

An auditory localization model based on high-frequency spectral cues.

We present in this paper a connectionist model that extracts interaural intensity differences (IID) from head-related transfer functions (HRTF) in the form of spectral cues to localize broadband high-frequency auditory stimuli, in both azimuth and elevation. A novel discriminative matching measure (DMM) is defined and optimized to characterize matching this IID spectrum. The optimal DMM approach and a novel back-propagation-based fuzzy model of localization are shown to be capable of localizing sources in azimuth, using only spectral IID cues. The fuzzy neural network model is extended to include localization in elevation. The use of training data with additive noise provides robustness to input errors. Outputs are modeled as two-dimensional Gaussians that act as membership functions for the fuzzy sets of sound locations. Error back-propagation is used to train the network to correlate input patterns and the desired output patterns. The fuzzy outputs are used to estimate the location of the source by detecting Gaussians using the max-energy paradigm. The proposed model shows that HRTF-based spectral IID patterns can provide sufficient information for extracting localization cues using a connectionist paradigm. Successful recognition in the presence of additive noise in the inputs indicates that the computational framework of this model is robust to errors made in estimating the IID patterns. The localization errors for such noisy patterns at various elevations and azimuths are compared and found to be within limits of localization blurs observed in humans.

Ear↗
PubMed1994

An expert system applied to the physiological analysis of early stage of beer fermentation.

A fuzzy expert system was applied to the knowledge analysis of yeast physiology in the early stage of beer fermentation, when the wort was aerated. We used ergosterol and glycogen concentration in the wort as a suitable marker of physiological state of the cell population. The amount of both compounds influences the rate of fermentation, cell growth and the final taste of beer. The concentrations of ergosterol and glycogen including the number of cells can not be measured immediately during the relatively short aeration period, and incomplete experimental data are therefore found in laboratory logbooks. We therefore suggested that the fuzzy relation between the directly measurable dissolved oxygen concentration and the rate of ergosterol or glycogen formation should be identified and a fuzzy expert system should be used to analyze the behavior of the yeast.

Beer↗
PubMed1997

Diagnosis of chronic liver disease from liver scintiscans by artificial neural networks.

Artificial neural networks were used in the diagnosis of chronic liver disease based on liver scintiscanning. One hundred and thirty-seven patients with chronic liver disease (12 with chronic persistent hepatitis, 39 with chronic aggressive hepatitis, and 86 with cirrhosis) and 25 healthy controls were studied. Sixty-five subjects (10 healthy controls, 20 patients with chronic hepatitis, and 35 patients with cirrhosis of the liver) were used in the establishment of a neural network. Liver scintiscans were taken starting 20 min after the intravenous injection of 111 MBq of Tc-99m-phytate. The neural network was used to evaluate five items judged from information on liver scintiscans: the ratio of the sizes of the left and right lobes, splenomegaly, radioactivity in the bone marrow, deformity of the liver and distribution of radioactivity in the liver. The neural network was designed to distinguish between three liver conditions (healthy liver, chronic hepatitis and cirrhosis) on the basis of these five items. The diagnostic accuracy with the neural network was 86% for patients with chronic hepatitis and 93% for patients with cirrhosis. With conventional scoring, the accuracy was 77% for patients with chronic hepatitis and 87% for patients with cirrhosis. Our findings suggest that artificial neural networks may be useful for the diagnosis of chronic liver diseases from liver scintiscans.

Case-Control Studies↗
PubMed2004

A comparative study of fuzzy classification methods on breast cancer data.

In this paper, we examine the performance of four fuzzy rule generation methods on Wisconsin breast cancer data. The first method generates fuzzy if-then rules using the mean and the standard deviation of attribute values with 92.2% correct classification rate. The second approach generates fuzzy if-then rules using the histogram of attributes values with 86.7% correct classification rate. The third procedure generates fuzzy if-then rules with certainty of each attribute into homogeneous fuzzy sets with 99.73% correct classification rate. In the fourth approach, only overlapping areas are partitioned with 62.57% correct classification rate. The first two approaches generate a single fuzzy if-then rule for each class by specifying the membership function of each antecedent fuzzy set using the information about attribute values of training patterns. The other two approaches are based on fuzzy grids with homogeneous fuzzy partitions of each attribute. The performance of each approach is evaluated on breast cancer data sets. Simulation results show that the simple grid approach has a high classification rate of 99.73%.

Algorithms↗
PubMed2003

A genetic algorithm based nearest neighbor classification to breast cancer diagnosis.

This paper presents an application of a hybrid approach (the genetic algorithms and the k-nearest neighbour) proposed by Ishbuchi to Wisconsin breast cancer data. For the diagnosis of breast cancer, the determination of the presence of benign/malignant breast tumors represents a very complex problem (even for an experienced cytologist). Therefore the automatic classification of benign and malignant symptoms is highly desirable as a valuable aid to assist oncologists in the decision making of the diagnosis of breast cancer. In this paper, the genetic algorithm based k-nearest neighbour method for classification of benign and malignant breast tumors is presented. The genetic-algorithm (GA) is used for finding a compact reference set by selecting a small number of reference patterns from a large number of training patterns in nearest neighbor classification. The GA simultaneously performs feature selection and pattern selection and prunes unnecessary features. The goal is to maximize the classification performance of the reference set and minimize the number of selected patterns and features. Results are also compared with a fuzzy-genetic approach where each reference patten represents a fuzzy if-then rule with a circular-cone-type membership function.

Algorithms↗
PubMed2002

A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases.

Development of computer-based medical inference systems is always confronted with some difficulties. In this paper, difficulties of designing an inference system for the diagnosis of arthritic diseases are described, including variations of disease manifestations under various situations and conditions. Furthermore, the need for a huge knowledge base would result in low efficiency of the inference system. We proposed a hierarchical model of the fuzzy inference system as a possible solution. With such a model, the diagnostic process is divided into two levels. The first level of the diagnosis reduces the scope of diagnosis to be processed by the second level. This will reduce the amount of input and mapping for the whole diagnostic process. Fuzzy relational theory is the core of this system and it is used in both levels to improve the accuracy.

Algorithms↗