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At least 433 records · Page 24Linked to original sources
PubMed1991

Neural network and conventional classifiers for fluorescence-guided laser angioplasty.

In laser angioplasty, fluorescence spectra of targeted tissue may be used to classify the tissue as atherosclerotic or normal and guide selective laser ablation of atherosclerotic plaque. Here, the ability of the back-propagation and K-nearest neighbors techniques to classify arterial fluorescence spectra is investigated. Both methods are competitive with other classification schemes. The relative performance of variations on both techniques is used to make inferences about the geometry of the classification task.

Angioplasty, Laser↗
PubMed2000

Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier.

We report a result on neural spike sorting under conditions where the signal-to-noise ratio is very low. The use of nonlinear energy operator enables the detection of an action potential, even when the SNR is so poor that a typical amplitude thresholding method cannot be applied. The superior detection ability facilitates the collection of a training set under lower SNR than that of the methods which employ simple amplitude thresholding. Thus, the statistical characteristics of the input vectors can be better represented in the neural-network classifier. The trained neural-network classifiers yield the correct classification ratio higher than 90% when the SNR is as low as 1.2 (0.8 dB) when applied to data obtained from extracellular recording from Aplysia abdominal ganglia using a semiconductor microelectrode array.

Action Potentials↗
PubMed1998

Integrated analyzer and classifier of glottographic signals.

Electroglottography (EGG) and photoglottography (PGG) are two plausible methods to study voice production for monitoring the patterns of laryngeal vibrations. It has been suggested that measures such as open quotient and speed quotient calculated from glottographic signals can provide useful information regarding pathological phonation. In this paper, an integrated analyzer and classifier of glottographic signals was implemented. The system makes it possible to calculate the measures from digitized EGG and PGG signals automatically in order to examine vocal fold abnormality. The system developed several techniques to extract features from glottographic signals and proposed a statistical classification method that can possibly aid the diagnosis process. To check the reliability of the system, a training set and a test set of glottographic signals from normal people and patients with recurrent/superior laryngeal paralysis were analyzed and classified by the system. The results showed that the system is a useful tool for quantitative study of phonatory pathophysiology and can be used by the examiner who is interested in the clinical examination of glottographic signals. Moreover, glottographic techniques may have some clinical applications in the quantitative documentation of phonatory function in patients with voice disorders but requires further evaluation before clinical application.

Electrodiagnosis↗
PubMed2004

BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.

Algorithms↗
PubMed2005

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data.

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap 632+ and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to approximately 96% correct classification rates with less than 10% of the original features.

Adult↗
PubMed2005

A compressed domain scheme for classifying block edge patterns.

Detecting and classifying edge components in images find a wide range of applications from perceptually improved coding schemes to content-based searches. In this paper, a fast and systematic scheme is proposed to classify the edge orientation of each block in discrete cosine transform (DCT)-compressed images. Derived from a simple, but acceptable, pixel-domain algorithm, the proposed scheme performs directly on DCT coefficient domain and, thus, saves the arithmetic operations by a considerable amount. The performance of the proposed method is validated by the experiments against various natural images.

Algorithms↗
PubMed2006

The fusion of large scale classified side-scan sonar image mosaics.

This paper presents a unified framework for the creation of classified maps of the seafloor from sonar imagery. Significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed. The techniques described are directly applicable to a range of remote sensing problems. Recent advances in side-scan data correction are incorporated to compensate for the sonar beam pattern and motion of the acquisition platform. The corrected images are segmented using pixel-based textural features and standard classifiers. In parallel, the navigation of the sonar device is processed using Kalman filtering techniques. A simultaneous localization and mapping framework is adopted to improve the navigation accuracy and produce georeferenced mosaics of the segmented side-scan data. These are fused within a Markovian framework and two fusion models are presented. The first uses a voting scheme regularized by an isotropic Markov random field and is applicable when the reliability of each information source is unknown. The Markov model is also used to inpaint regions where no final classification decision can be reached using pixel level fusion. The second model formally introduces the reliability of each information source into a probabilistic model. Evaluation of the two models using both synthetic images and real data from a large scale survey shows significant quantitative and qualitative improvement using the fusion approach.

Acoustics↗
PubMed2006

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure.

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers that generalize well.

Algorithms↗
PubMed2003

Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project.

In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair).

Adaptation, Physiological↗
PubMed2003

Analyzing EEG signals using the probability estimating guarded neural classifier.

This paper introduces a neural network architecture for classifying feature vectors symbolizing portions (or segments) of an electroencephalogram (EEG) trace of a human subject. This classification task is the one that is typically required when developing a so-called brain-computer interface (BCI), which analyzes the EEG signals of a subject in order to "understand" the subject's thoughts. However, instead of merely saying which "category of thoughts" (i.e., which class) the respective input feature vector belongs to, the network described here estimates the probabilities of an EEG segment being associated with each individual class. The network, which is called PeGNC (for probability estimating guarded neural classifier), is tested with two kinds of experiments. In the first experiment, the alpha-rhythm associated with a human subject closing the eyes is detected online with the help of a frequency-based representation. Since the EEG signal is, in general, always a mixture of numerous action potentials generated simultaneously and it is, thus, very likely that mental activities result in overlapping classes, it is reasonable to believe that the PeGNC network--which does not select any one single class, but determines probability values for each mental category--is particularly suitable for this kind of EEG analysis. The second experiment deals with this issue on the basis of an offline analysis of simulated data.

Adult↗
PubMed2006

Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

Algorithms↗
PubMed2004

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction.

Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.

Journal Article↗
PubMed2005

On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers.

In Kernel-based Nonlinear Subspace (KNS) methods, the subspace dimensions have a strong influence on the performance of the subspace classifier. In order to get a high classification accuracy, a large dimension is generally required. However, if the chosen subspace dimension is too large, it leads to a low performance due to the overlapping of the resultant subspaces and, if it is too small, it increases the classification error due to the poor resulting approximation. The most common approach is of an ad hoc nature, which selects the dimensions based on the so-called cumulative proportion computed from the kernel matrix for each class. In this paper, we propose a new method of systematically and efficiently selecting optimal or near-optimal subspace dimensions for KNS classifiers using a search strategy and a heuristic function termed the Overlapping criterion. The rationale for this function has been motivated in the body of the paper. The task of selecting optimal subspace dimensions is reduced to finding the best ones from a given problem-domain solution space using this criterion as a heuristic function. Thus, the search space can be pruned to very efficiently find the best solution. Our experimental results demonstrate that the proposed mechanism selects the dimensions efficiently without sacrificing the classification accuracy.

Algorithms↗
PubMed2004

Evolutionary design of a fuzzy classifier from data.

Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification system directly from data without any a priori knowledge or assumptions on the distribution of the data. At the beginning of the algorithm, the fuzzy classifier is empty with no rules in the rule base and no membership functions assigned to fuzzy variables. Then, rules and membership functions are automatically created and optimized in an evolutionary process. To accomplish this, parameters of the variable input spread inference training (VISIT) algorithm are used to code fuzzy systems on the training data set. Therefore, we can derive each individual fuzzy system via the VISIT algorithm, and then search the best one via genetic operations. To evaluate the fuzzy classifier, a fuzzy expert system acts as the fitness function. This fuzzy expert system can effectively evaluate the accuracy and compactness at the same time. In the application section, we consider four benchmark classification problems: the iris data, wine data, Wisconsin breast cancer data, and Pima Indian diabetes data. Comparisons of our method with others in the literature show the effectiveness of the proposed method.

Algorithms↗
PubMed2005

Evolving binary classifiers through parallel computation of multiple fitness cases.

This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.

Algorithms↗
PubMed2006

Genetic programming for simultaneous feature selection and classifier design.

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.

Algorithms↗
PubMed1981

Diagnostic value of the Hemalog D-90 in classifying acute childhood leukaemias.

Accurate classification of the acute leukaemias is dependent not only on morphological characteristics, but also on cytochemical staining properties of the blasts in peripheral blood and bone marrow. This study was undertaken to evaluate the Hemalog D-90, an automated cytochemistry system for white cell differential counts, as an adjunct to conventional microscopy in the classification of acute childhood leukaemias. Fifty-two patients were classified according to morphology and cytochemical reactions of the cells obtained from peripheral blood. In 29 with classical acute lymphoblastic leukaemia (ALL), the Hemalog D showed the lymphoblasts to be non-peroxidase staining and these were recorded mainly as lymphocytes. In contrast, the blasts of eight patients with acute myeloblastic leukaemia (AML) were peroxidase positive, being recorded as neutrophils. Of the remaining 15 patients, 10 were classified as "Probably ALL' because they lacked some of the morphological or cytochemical criteria for classical ALL, while five were unclassifiable. However, in all these 15 patients the Hemalog D results were similar to those obtained in patients with classical ALL. We concluded that the Hemalog D was a useful adjunct in distinguishing ALL from AML, including the morphologically indistinct types.

Acute Disease↗
PubMed1990

CD4+ T-cell clones from autoimmune thyroid tissue cannot be classified according to their lymphokine production.

In order to define whether CD4+ T cells from autoimmune and non-autoimmune thyroid tissue could be classified according to their mediator production, lymphokine production was studied in 63 thyroid-derived CD4+ T-cell clones from four patients with Graves' disease, one with Hashimoto's thyroiditis, and one with non-toxic goitre (9-12 clones per patient). The production of interleukin 2 (IL-2), gamma interferon (IFN-gamma), tumour necrosis factor alpha (TNF-alpha), lymphotoxin (LT), interleukin 6 (IL-6) and transforming growth factor beta (TGF-beta) was assessed at the mRNA level by slot-blot analysis in unstimulated clones as well as after activation with monoclonal anti-CD3 (OKT3) and IL-2. No lymphokine production was found in unstimulated clones, whereas 56% of the clones produced all six lymphokines simultaneously after stimulation. In the remaining 44% usually not more than one lymphokine was missing from the complete panel. Lymphokine mRNA concentrations varied between different clones and different patients, but, in this small sample, not between the diseases from which the clones were originated. There was a significant correlation between IL-6, LT, and IL-2 mRNA levels and T-cell helper function, which was estimated by the stimulation of thyroid microsomal autoantibody production using autologous peripheral B cells. TGF-beta and IFN-gamma mRNA expression was unrelated to T-cell help. The results demonstrate that intrathyroid T cells from autoimmune and non-autoimmune thyroid disorders cannot be classified according to their lymphokine production, unlike some results with in vitro-induced mouse T-cell clones, where two populations, Th1 and Th2, have been described. Single T cells are capable of producing a whole panel of lymphokines and thus are capable of triggering a multitude of different processes.

CD4-Positive T-Lymphocytes↗