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Results for “Classification Algorithms”

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At least 361 records · Page 20Linked to original sources
PubMed2003

Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques.

A technique is proposed that allows automatic decomposition of electromyographic (EMG) signals into their constituent motor unit action potential trains (MUAPTs). A specific iterative algorithm with a classification method using fuzzy-logic techniques was developed. The proposed classification method takes into account imprecise information, such as waveform instability and irregular firing patterns, that is often encountered in EMG signals. Classification features were determined by the combining of time position and waveform information. Statistical analysis of inter-pulse intervals and spike amplitude provided an accurate estimation of features used in the classification step. Algorithm performance was evaluated using simulated EMG signals composed of up to six different discharging motor units corrupted with white noise. The algorithm was then applied to real signals recorded by a high spatial resolution surface EMG device based on a Laplacian spatial filter. On six groups of 20 simulated signals, the decomposition algorithm performed with a maximum and an average mean error rate of 2.13% and 1.37%, respectively. On real surface EMG signals recorded at different force levels (from 10% to 40% of the maximum voluntary contraction), the algorithm correctly identified 21 MUAPTs, compared with the 29 MUAPTs identified by an experienced neurophysiologist. The efficiency of the decomposition on surface EMG signals makes this method very attractive for non-invasive investigation of physiological muscle properties. However, it can also be used to decompose intramuscularly recorded EMG signals.

Action Potentials↗
PubMed1992

The exposed total knee replacement prosthesis: a new classification and treatment algorithm.

The increased frequency of total knee replacement arthroplasty (TKRA) has been reflected in the number of patients with exposed prostheses referred to this unit. An algorithm has been developed to assist in the preoperative assessment of the wound and this has been tested on 25 patients with wound breakdown following TKRA. The grade of exposure so derived predicts the most appropriate surgical management. The algorithm, grading system and proposed management are described.

Algorithms↗
PubMed2006

Trapeziometacarpal arthroscopy: a classification and treatment algorithm.

Arthroscopic assessment of the CMC joint allows direct visualization of all components of the joint, including synovium, articular surfaces, ligaments, and the joint capsule. It also allows for the extent of joint pathology to be evaluated and staged with intraoperative management decisions made based on this information. The author recommends this arthroscopic staging to ensure better judgment of this condition in order to provide the most adequate treatment option to patients who have this disabling condition. Future studies assessing the clinical long term results using arthroscopy will likely ensure its place in the treatment armamentarium for trapeziometacarpal osteoarthritis.

Algorithms↗
PubMed2005

Clinical, immunophenotypic, and genetic analysis of adult lymphomas with morphologic features of Burkitt lymphoma.

A prompt distinction of Burkitt lymphoma (BL) versus diffuse large B cell lymphomas (DLBCL) has important clinical implications; however, this distinction can be difficult. We analyzed 74 adult gray zone and 10 reference pediatric BL using immunohistochemistry (Ki-67, CD10, bcl2, bcl6) and fluorescence in situ hybridization (FISH) for MYC, BCL2, and BCL6 breakpoints. Two algorithms for classification were followed: algorithm A used a two-step review by four hemato-pathologists and algorithm B a set of only biologic markers (Ki-67 > or = 90%, CD10+, bcl6+, bcl2-, MYC breakpoint+, BCL2 and BCL6 breakpoint-). Both algorithms categorized all reference cases as BL. In the adult group, algorithm A resulted in 21 adult BL and 52 DLBCL and algorithm B in 23 BL and 51 "non-Burkitt" lymphomas (nBL); 9 cases (12%) contained two different translocations and were categorized as nBL in algorithm B. Fifteen cases (20%) fulfilled the BL criteria of both algorithms. Although not considered as BL according to both algorithms, many other lymphomas showed nonetheless a phenotypic and/or genetic shift to BL. BL according to algorithm B was more homogeneous with respect to clinical presentation (gender and localization) than BL defined by algorithm A. Our data suggest that only a few cases of these gray zone lymphomas represent true de novo BL. Immunohistochemistry for Ki-67, CD10, and bcl2 with analysis of MYC and preferably also BCL2 and BCL6 may be advised as a marker panel for this diagnostic dilemma.

Adolescent↗
PubMed1991

Prediction of use of psychiatric services: application of the CART (classification and regression trees) algorithm.

A computerized recursive partitioning program called Classification and Regression Trees (CART) was used to identify potential high users of services among low-income psychiatric outpatients. Sociodemographic variables, clinical variables (e.g., psychiatric diagnosis and type of presenting complaint), source of referral and the most recent psychiatric treatment setting used were studied. Discharge from inpatient psychiatric treatment right before admission to outpatient psychiatric treatment was found to be the most consistent, the most powerful and the only necessary predictor of high use of outpatient psychiatric services.

Adult↗
PubMedDate not supplied

[Design of an algorithm for the numerical classification of viruses].

The data are presented dealing with the construction of algorithm of numerical classification of RNA- and DNA-containing viruses of vertebrates. The possibility of using of a limited number of parameters with certain coefficients was demonstrated. Suitable parameters for this purpose were elucidated and coefficients (weights) for them determined.

Computers↗
PubMed1998

Encoded pattern classification using constructive learning algorithms based on learning vector quantization.

A novel encoding technique is proposed for the recognition of patterns using four different techniques for training artificial neural networks (ANNs) of the Kohonen type. Each template or model pattern is overlaid on a radial grid of appropriate size, and converted to a two-dimensional feature array which then acts as the training input to the ANN. The first technique employs Kohonen's self-organizing network, each neuron of which is assigned, after training, the label of the model pattern. It is found that a graphical plot of the labels of the neurons exhibits clusters (which means in effect that the feature array pertaining to distorted versions of the same pattern belongs to a specific cluster), thereby justifying the coding strategy used in this paper. When the new, unknown pattern is input to the network, it is classified to have the same label of the neuron whose corresponding model pattern is closest to the given pattern. In an attempt to reduce the computational time and the size of the network, and simultaneously improve accuracy in recognition, Kohonen's learning vector quantization (LVQ) algorithm is used to train the ANN. To further improve the network's performance and to realize a network of minimum size, two constructive learning algorithms, both based on LVQ, are proposed: (1) multi-step learning vector quantization (MLVQ), and (2) thermal multi-step learning vector quantization (TLVQ). When the proposed algorithms are applied to the classification of noiseless and noisy (and distorted) patterns, the results demonstrate that the pattern encoding strategy and the suggested training techniques for ANNs are efficient and robust. For lack of space, only the most essential results are presented here. For details, see Ganesh Murthy and Venkatesh (1996b).

Journal Article↗
PubMed2006

Learning outdoor color classification.

We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training image. Then, using a simple diagonal illumination model, the illuminants in a new scene that contains some of the surface classes seen in the training image are estimated in a maximum likelihood framework using the Expectation Maximization algorithm. We also show how to impose priors on the illuminants, effectively computing a maximum a posteriori estimation. Experimental results are provided to demonstrate the performance of our classification algorithm in the case of outdoor images.

Algorithms↗
PubMed2003

A CART-based approach to discover emerging patterns in microarray data.

MOTIVATION: Cancer diagnosis using gene expression profiles requires supervised learning and gene selection methods. Of the many suggested approaches, the method of emerging patterns (EPs) has the particular advantage of explicitly modeling interactions among genes, which improves classification accuracy. However, finding useful (i.e. short and statistically significant) EP is typically very hard. METHODS: Here we introduce a CART-based approach to discover EPs in microarray data. The method is based on growing decision trees from which the EPs are extracted. This approach combines pattern search with a statistical procedure based on Fisher's exact test to assess the significance of each EP. Subsequently, sample classification based on the inferred EPs is performed using maximum-likelihood linear discriminant analysis. RESULTS: Using simulated data as well as gene expression data from colon and leukemia cancer experiments we assessed the performance of our pattern search algorithm and classification procedure. In the simulations, our method recovers a large proportion of known EPs while for real data it is comparable in classification accuracy with three top-performing alternative classification algorithms. In addition, it assigns statistical significance to the inferred EPs and allows to rank the patterns while simultaneously avoiding overfit of the data. The new approach therefore provides a versatile and computationally fast tool for elucidating local gene interactions as well as for classification. AVAILABILITY: A computer program written in the statistical language R implementing the new approach is freely available from the web page http://www.stat.uni-muenchen.de/~socher/

Algorithms↗
PubMed2005

An optimized experimental protocol based on neuro-evolutionary algorithms application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment.

OBJECTIVE: This paper aims to present a specific optimized experimental protocol (EP) for classification and/or prediction problems. The neuro-evolutionary algorithms on which it is based and its application with two selected real cases are described in detail. The first application addresses the problem of classifying the functional (FD) or organic (OD) forms of dyspepsia; the second relates to the problem of predicting the 6-month follow-up outcome of dyspeptic patients treated by helicobacter pylori (HP) eradication therapy. METHODS AND MATERIAL: The database built by the multicentre observational study, performed in Italy by the NUD-look Study Group, provided the material studied: a collection of data from 861 patients with previously uninvestigated dyspepsia, being referred for upper gastrointestinal endoscopy to 42 Italian Endoscopic Services. The proposed EP makes use of techniques based on advanced neuro-evolutionary systems (NESs) and is structured in phases and steps. The use of specific input selection (IS) and training and testing (T and T) techniques together with genetic doping (GenD) algorithm is described in detail, as well as the steps taken in the two benchmark and optimization protocol phases. RESULTS: In terms of accuracy results, a value of 79.64% was achieved during optimization, with mean benchmark values of 64.90% for the linear discriminant analysis (LDA) and 68.15% for the multi layer perceptron (MLP), for the classification task. A value of 88.61% was achieved during optimization for the prediction task, with mean benchmark values of 49.32% for the LDA and 70.05% for the MLP. CONCLUSIONS: The proposed EP has led to the construction of inductors that are viable and usable on medical data which is representative but highly not linear. In particular, for the classification problem, these new inductors may be effectively used on the basal examination data to support doctors in deciding whether to avoid endoscopic examinations; whereas, in the prediction problem, they may support doctors' decisions about the advisability of eradication therapy. In both cases the variables selected indicate the possibility of reducing the data collection effort and also of providing information that can be used for general investigations on symptom relevance.

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↗
PubMed1998

Efficient adaptive learning for classification tasks with binary units.

This article presents a new incremental learning algorithm for classification tasks, called NetLines, which is well adapted for both binary and real-valued input patterns. It generates small, compact feedforward neural networks with one hidden layer of binary units and binary output units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary and real-valued input patterns. An implementation for problems with more than two classes, valid for any binary classifier, is proposed. The generalization error and the size of the resulting networks are compared to the best published results on well-known classification benchmarks. Early stopping is shown to decrease overfitting, without improving the generalization performance.

Algorithms↗
PubMed2004

Necrotising fasciitis: a new management algorithm based on clinical classification.

Necrotising fasciitis is a rare infection of the subcutaneous tissues. If untreated, it is invariably fatal, and thus a high index of suspicion for the diagnosis is required. The disease's manifestation can range from a fulminant presentation to a subtle and insidious development. The priority in every case is to proceed to radical surgical debridement. On review of the literature and based on our clinical experience, we propose a new classification based on clinical presentation and suggest an algorithm to facilitate the management of this devastating condition. Increasing awareness should be given to the management of the large wounds resulting from the surgical debridement of necrotising fasciitis.

Abdominal Muscles↗
PubMed1980

Classification of ankle fractures: an algorithm.

In 1950, Lauge-Hansen devised a classification of ankle fractures based on the position of the foot and the deforming force at the time of injury. This has been widely accepted by orthopedists, but is not in general use by radiologists. An algorithm based on his classification that allows rapid assessment of the mechanism of injury in 90%-95% of cases has been devised by the authors. Identification of the fractures and classification of the type of injury allows diagnosis of the otherwise occult ligamentous injuries. On the basis of location and appearance of the fibular fracture, the four types of injuries are immediately identified: pronation-abduction, pronation-lateral rotation, supination-adduction, and supination-lateral rotation. Subsequent assessment for medial malleolar fracture and then posterior malleolar fracture defines the stage of completeness of the injury and localizes the sites of ligamentous disruption.

Ankle↗