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PubMed1999

A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques.

In this paper, a hybrid approach is presented for discriminating a few upper limb movements by processing the electromyographic (EMG) signals from selected shoulder muscles. Statistical techniques, such as the Generalized Likelihood Ratio test, the Principal Component Analysis, autoregressive parametric modeling techniques and cepstral analysis techniques, combined with a fuzzy logic based classifier (the Abe-Lan network) are used to construct low-dimensional feature spaces with high classification rates. The experimental results show the ability of the algorithm to correctly classify all the EMG patterns related to the selected planar arm pointing movements. Moreover, the structure presented offers promise for real-time applications because of the low computation costs of the overall algorithm.

Adult↗
PubMed1984

The distribution of the paternity index as a basis for evaluation of sequential testing in paternity analysis.

Several procedures for evaluation of paternity testing data have been suggested in the literature, the majority of them being based on the paternity index statistic (L) or some transform of it. A major problem has been that the true distribution of the paternity index has not been known, making it difficult to perform quantitative evaluations of different procedures. We present an algorithm for computation of the distribution of the paternity index within the limits of a completely controlled amount of approximation. Using this algorithm we evaluate the power and the rate of erroneous classifications of a standard routine test based on a fixed number of genetic marker systems. The efficiency of this standard test procedure is compared to a stepwise (sequential) procedure where in each step one or several marker systems are scored for the mother-child-putative father trio. We suggest that a sequential strategy for testing may be more efficient than one that is based on a fixed number of systems. A sequential procedure can provide information about the accused man's state of paternity in a considerably larger fraction of cases without a substantial increase of the frequency of incorrect classifications. In addition, the cost measured as the average number of marker systems that has to be tested for each trio may be lower in the case of sequential testing than with a fixed number of systems.

Blood Group Antigens↗
PubMed1990

Echographic tissue characterization in diffuse parenchymal liver disease: correlation of image structure with histology.

Seventy livers were examined in vitro using a computerized ultrasound B-mode data acquisition and analysis system. For tissue characterization, statistical parameters from pattern recognition algorithms describing image brightness and image structure were used. Reference classification based on histopathology as well as on chemical/morphometrical analysis led to the diagnostic classes of normal, fatty liver, fibrosis/cirrhosis and fatty fibrosis/cirrhosis. Comparing the two reference methods for ultrasound tissue characterization, reclassification based on chemical/morphometrical analysis resulted in a significant increase in diagnostic accuracy. The strong correlations between statistical ultrasound image parameters and morphometrical features reflect the relevance of our statistical approach to ultrasound tissue characterization.

Fatty Liver↗
PubMed1992

Expert system design in hematology diagnosis.

A two-part study was designed to test the hypothesis that sufficient information is available from a modern hematology analyzer (the Coulter STKS) to reach a reliable intermediate conclusion which can be used as input to the next decision-making level in the design of a high-performance expert system for hematology diagnosis. In phase one, we analyzed the performance of three probabilistic systems (using Bayes' rule) which interpret STKS data: a control system which took the traditional approach of classifying cases into specific diagnoses, and two test systems which were designed to reach only an intermediate conclusion but not a final diagnosis. One of the test systems classified cases into "textbook categories" of disease and the other utilized defined diagnostic patterns. The systems were tested with 150 cases. The pattern approach ranked the correct choice first in 141 of 150 cases (94%). In phase two, we abandoned Bayes' rule, reformulated the pattern approach into a heuristic classification system, and tested its reliability on 820 cases. The algorithm of the reformulated system was able to classify all 820 cases into the same predominant pattern as a panel of three experienced laboratory hematologists.

Algorithms↗
PubMed1993

Wave packets analysis of two-dimensional protein maps: a new approach to study the diversity of immunoglobulins.

This report describes a mathematical approach for classifying two-dimensional (2D)-protein maps without spot detection or pattern matching. Analysis of electrophoretograms was performed using wave packet decompositions of the signals. The scanned images were automatically decomposed into a set of sub-images organized in a tree structure. Each sub-image contained relevant information such as its energy, or entropy. Moreover the node position itself of the sub-image reflected a frequency localization. A distance was then defined using the tree repartition of these quantities. Finally a statistical clustering on the tree structures was performed, terminating with a classification of the images according to their repartition frequencies. The algorithm has been applied to classify immunoglobulin (Ig) light chain patterns and proved useful to automatically detect monoclonal, oligoclonal or polyclonal Igs.

Algorithms↗
PubMed1995

Segmentation of mammograms using multiple linked self-organizing neural networks.

A possible first stage in the analysis of the mammographic scene is its segmentation into four major components: background (the nonbreast area), pectoral muscle, fibroglandular region (parenchyma), and adipose region. An algorithm has been developed for this task. It is based on the classification of a feature vector constructed from statistical measures of texture calculated at two window sizes. Separate self-organizing neural networks are trained on sample data taken from each of the four regions. The feature vectors from the entire mammogram are then classified with the trained networks linked via a decision logic. To overcome the variability of texture between mammograms the algorithm uses data from a mammogram to classify itself in a staged approach consisting of several binary decisions. The training regions for each successive stage are determined from geometric information produced by the previous stages. The dataset in the study consisted of thirty (fifteen pairs) digitized normal mammograms of variable radiographic appearance. As a measure of performance, the outlines of the parenchyma were compared to those drawn by a radiologist experienced in reading mammograms. Comparison of the areas and perimeters generated by the human and computer observers gives a relationship with correlation coefficients of 0.74 and 0.59 for each measure, respectively. The overlapping areas of the parenchymas segmented by the observers normalized by the combined area was also calculated for each case. The mean and standard deviation of this measure was 0.69 +/- 0.12.

Adipose Tissue↗
PubMed2000

Fuzzy EMG classification for prosthesis control.

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.

Algorithms↗
PubMed1986

An algorithm for the management of scoliosis.

Scoliosis is a lateral and rotary deformity of the spine that is often found in children. Treatment of this deformity is based on the principle of early recognition and prevention, although surgical correction may be warranted in the case of progressive curves. There is no scientific evidence that spinal manipulative therapy (SMT) has any effect on curve progression in patients with idiopathic scoliosis; however, there is clinical evidence that SMT is a useful treatment for those patients who have an associated mechanical backache. This paper reviews the classification, natural history, pathogenesis, and clinical and radiological assessment of scoliosis. An algorithm for the management of scoliosis by chiropractors is presented, and illustrative cases from a scoliosis clinic in a university hospital are used to reinforce important clinical principles.

Adolescent↗
PubMed2000

A practical primary care approach to hematuria in children.

Although hematuria is a common finding in the unselected population of children, the approach to evaluation is quite variable. Changes in the practice of primary care medicine in the United States mandate an approach to common office problems that is practical and realistic. This review addresses three areas: the current approach to evaluation of hematuria in children, a classification of children with hematuria into four distinct and easily identified clinical categories, and the development of an algorithm for application in the primary care setting. Each category is discussed relative to the more-common etiologies of hematuria, with recommendations for appropriate evaluation as well as suggestions of an appropriate referral to the nephrologist. An algorithm is proposed that provides a practical, systematic approach to the problem without the requirement for a specific diagnosis in every patient. The proposed classification and approach to the evaluation of children with hematuria should help simplify and clarify a potentially complex process.

Child↗
PubMed1995

Definition and application of a fourier domain texture measure: applications to histological image segmentation.

A texture measure is defined for the purpose of two-dimensional histological image classification, based upon a novel exploitation of the modulus of the Fourier transform. Its implementation in the form of an algorithm is presented and discussed, and employed to classify a set of histological images. The results, which were obtained by the analysis and classification of different mammalian tissue types, compare favourably with established texture recognition techniques. Furthermore, the results suggest that the method has a comparable performance to other techniques at reduced storage and time costs for images of a high grey level resolution.

Algorithms↗
PubMedDate not supplied

[Empirical French criteria for psychoses. III. Algorithms and decision tree].

The present report presents the final results of an empirical investigation initiated to establish operational definitions for schizophrenia and other non-affective psychoses of classical French nosology. For each category, the authors provide diagnostic criteria and algorithms. In addition, they propose a decision tree for the differential diagnosis of psychotic features included in the French classification of mental disorders.

Affective Disorders, Psychotic↗
PubMed1990

Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps.

We extend the neural concepts of topological feature maps towards self-organization of auto-associative memory and hierarchical pattern classification. As is well-known, topological maps for statistical data sets store information on the associated probability densities. To extract that information we introduce a recurrent dynamics of signal processing. We show that the dynamics converts a topological map into an auto-associative memory for real-valued feature vectors which is capable to perform a cluster analysis. The neural network scheme thus developed represents a generalization of non-linear matrix-type associative memories. The results naturally lead to the concept of a feature atlas and an associated scheme of self-organized, hierarchical pattern classification.

Algorithms↗
PubMed1990

Automatic computer detection of clustered calcifications in digital mammograms.

The automatic detection of clusters of calcifications in digital mammograms has been investigated using image analysis techniques. The calcifications were segmented from the background of normal breast structure in the mammogram using a local area thresholding process. This procedure also identified other breast structures and the digital image properties of all segmented objects were analysed to extract clusters of calcifications. Seventy five clinical mammograms were digitised. These were divided into training and test sets of 25 and 50 films respectively. The results for the test set of 50 complete clinical mammograms show that the computer system achieves a 25/25 true positive film classification (i.e. those containing clusters of calcifications) with false positive clusters detected in 4/50 films. There were no false negative film classifications.

Algorithms↗
PubMed1998

Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis.

A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects from the background using dynamic thresholding of the P2/A-histogram, where P2/A is a global roundness measure. Then the image is transformed into principal component hue, defined as the angle around the first principal component. This image is used to segment positive and negative objects. The method is fully automatic and the principal component approach makes it robust with respect to illumination and focus settings. An independent test set consisting of images grabbed with different focus and illumination for each field of view was used to test the method, and the proposed method showed less variation than the intraoperator variation using supervised Maximum Likelihood classification.

Algorithms↗
PubMed1994

Digital signal processing chip implementation for detection and analysis of intracardiac electrograms.

The adoption of digital signal processing (DSP) microchips for detection and analysis of electrocardiographic signals offers a means for increased computational speed and the opportunity for design of customized architecture to address real-time requirements. A system using the Motorola 56001 DSP chip has been designed to realize cycle-by-cycle detection (triggering) and waveform analysis using a time-domain template matching technique, correlation waveform analysis (CWA). The system digitally samples an electrocardiographic signal at 1000 Hz, incorporates an adaptive trigger for detection of cardiac events, and classifies each waveform as normal or abnormal. Ten paired sets of single-chamber bipolar intracardiac electrograms (1-500 Hz) were processed with each pair containing a sinus rhythm (SR) passage and a corresponding arrhythmia segment from the same patient. Four of ten paired sets contained intraatrial electrograms that exhibited retrograde atrial conduction during ventricular pacing; the remaining six paired sets of intraventricular electrograms consisted of either ventricular tachycardia (4) or paced ventricular rhythm (2). Of 2,978 depolarizations in the test set, the adaptive trigger failed to detect 6 (99.8% detection sensitivity) and had 11 false triggers (99.6% specificity). Using patient dependent thresholds for CWA to classify waveforms, the program correctly identified 1,175 of 1,197 (98.2% specificity) sinus rhythm depolarizations and 1,771 of 1,781 (99.4% sensitivity) abnormal depolarizations. From the results, the algorithm appears to hold potential for applications such as real-time monitoring of electrophysiology studies or detection and classification of tachycardias in implantable antitachycardia devices.

Algorithms↗
PubMed1992

Case-mix groups for VA hospital-based home care.

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.

Aftercare↗
PubMed1999

Patients with newly diagnosed carcinoma of the breast: validation of a claim-based identification algorithm.

The objectives of this study were to validate a claims-based algorithm for identification of patients with newly diagnosed carcinoma of the breast and to optimize the algorithm. Claims data from all females aged 21 years or older who enrolled in a large California health maintenance organization during the study period from October 1, 1994 through March 31, 1996 were analyzed. Medical records of the patients identified through the claims-based algorithm were reviewed to determine whether the patients were correctly identified. The initial algorithm had a positive predictive value of 84% which was similar to the previous study. The percentages of correct identification significantly increased with the patient's age at diagnosis. Other patient demographic characteristics and facility characteristics were not related to the accuracy of the identification. Using a classification tree procedure and additional information from the false-positive cases, the initial algorithm was modified for improvement. The best-modified algorithm had a positive predictive value of 92% while only 0.5% (4/837) of the true-positive cases were excluded. The results once again demonstrated that patients with newly diagnosed carcinomas of the breast can be identified using claims data. These databases provide an efficient and effective tool for performing health services studies on large patient populations.

Adult↗