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Algorithms for the analysis of polysomnographic recordings with customizable criteria

EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 10133-10146页

作者： Otero, A. Felix, P. Alvarez, M. R. Univ San Pablo CEU Dept Informat & Commun Syst Engn Madrid 28668 Spain Univ Santiago de Compostela Dept Elect & Computac Santiago De Compostela 15782 Spain

The diagnosis of Sleep Apnoea-Hypopnoea Syndrome requires the visual inspection of a recording containing a large number of physiological parameters of the patient - the polysomnogram. The purpose of this visual inspection is the identification and characterization of different types of pathological events that occur over these parameters. These events are defined by a set of morphological criteria. Based on these criteria, commercial tools have been developed to support clinicians in the task of visually inspecting polysomnograms. This article argues that the standard morphological criteria are just guiding recommendations that experienced physicians often adapt to each specific diagnostic context. Thus, tools that support the analysis of polysomnograms ideally should use flexible criteria that could be easily customizable by clinicians. In this paper, we propose algorithms capable of identifying pathological events relevant in the diagnosis of SAHS using custom criteria that are acquired directly from the clinician. These algorithms take advantage of the Fuzzy Set Theory to capture and handle the vagueness and uncertainty that are characteristics of medical knowledge. Knowledge acquisition using the traditional linguistic approach of the Fuzzy Sets Theory is supported by a desktop tool. However, the authors feel that some of the criteria that need to be acquired are more visual in nature than linguistic. An alternative mechanism for the visual acquisition of these criteria is proposed. Finally, when presenting the pathological events that have been identified, the tool uses several visual metaphors designed to simplify visual inspection of the polysomnogram. We have validated our proposal over 69 h of polysomnographic recordings arising from 12 patients that were subjected to a sleep study. 95.7% of the events identified were correct detections. The rate of false negatives was 1.6%. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Knowledge acquisition Sleep Apnoea-Hypopnoea syndrome Fuzzy sets structural pattern recognition Clinical user interfaces

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A METHOD FOR INFERRING PROBABILISTIC CONSENSUS STRUCTURE WITH APPLICATIONS TO MOLECULAR SEQUENCE DATA

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pattern recognition 1993年第4期26卷 643-654页

作者： CHIU, DKY HARAUZ, G UNIV GUELPH DEPT MOLEC BIOL & GENET GUELPH N1G 2W1 ONTARIO CANADA

The notion of probabilistic consensus structure, a probabilistic model satisfying certain domain constraints, is defined as a statistical relational graph inferred from an ensemble of sequences (or strings). The probabilistic consensus structure is a special form of random graph indicating both the statistical variation and the structural relationship of its components. It is inferred from unsupervised learning using both statistical and given domain constraints. Thus, in principle, its detection is more reliable than using either statistical analysis or domain knowledge (such as heuristic search) alone. An algorithm inferring the probabilistic consensus structure from a random n-tuple is designed based on the detection of statistical interdependency under a given structural constraint criterion. A circular diagram is proposed to facilitate visual interaction with the user for detecting global structural characteristics. The consensus structure can be used as an extended higher-order representation of the random n-tuple for statistical and structural pattern recognition and it represents a form of inherent interdependency of the domain. Additional directionality from human experts can be imposed to generate a causal model. The method is applied to molecular modeling of ribonucleic acid (RNA) from aligned homologous molecular sequences. Experiments based on hypothetical and real molecular structures involving transfer and ribosomal RNA sequences demonstrate the detection of secondary structural domains, as well as of tertiary interactions.

关键词： PROBABILISTIC CONSENSUS STRUCTURE structural CONSTRAINT STATISTICAL INTERDEPENDENCY structural pattern recognition MOLECULAR MODELING RNA

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DEVELOPMENT TOOLS FOR A MODEL DIRECTED WORKPIECE recognition SYSTEM

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pattern recognition 1986年第4期19卷 267-278页

作者： FREYTAG, R HAETTICH, W WANDRES, H Fraunhofer Institut fuer Informations- und Datenverarbeitung Sebastian-Kneipp Strasse 12–1 7500 Karlsruhe F.R. Germany

The application of industrial robots for automation can be greatly extended if the robot is equipped with a visual sensor that allows it to recognize and locate parts and workpieces. structural recognition methods are frequently used for the recognition of industrial parts and the recognition is guided by an abstract description (the model) of the workpiece. In an industrial environment not specially trained personnel has to be able to establish the model effectively and easily. The paper describes tools that were developed to generate and optimize the model data interactively, which are necessary to drive workpiece recognition systems. The power of these tools for interactive model generation is demonstrated by an example of a typical industrial recognition task. The tools can be further improved towards automatic model generation.

关键词： pattern recognition structural pattern recognition Model directed pattern recognition Evolution strategy Workpiece recognition Overlapping workpieces Model generation Model optimization

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A structural knowledge-based proposal for the identification and characterization of apnoea episodes

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APPLIED SOFT COMPUTING 2012年第1期12卷 516-526页

作者： Otero, Abraham Felix, Paulo Barro, Senen Zamarron, Carlos Univ San Pablo CEU Dept Informat & Commun Syst Engn Madrid 28668 Spain Univ Santiago de Compostela Dept Elect & Comp Sci Santiago De Compostela 15782 Spain Univ Hosp Complex Santiago de Compostela Div Resp Med Santiago De Compostela 15782 Spain

This paper presents a new pattern recognition approach to the identification and characterization of multiple signal patterns in the domain of Sleep Apnoea Syndrome. This approach has been employed to identify apnoeas (cessations in the sleeping patient's respiratory airflow) and to relate them with the drops in blood oxyhaemoglobin saturation they give rise to. As a starting point, our algorithms take a projection over a computational representation of the morphological criteria which characterize apnoeas and desaturations. These criteria are obtained directly from a physician. The fuzzy set theory allows us to represent and handle the vagueness of the medical knowledge on which this proposal is based, and the constraint satisfaction problem formalism supplies a computable representation for this knowledge. Thanks to the structural nature of the proposal, a detailed characterization of any event identified can be carried out;this may serve as a starting point for obtaining a deeper understanding of the physiopathological phenomena underlying Sleep Apnoea Syndrome. It has also made it possible to construct a graphical tool with which medical staff can easily edit the criteria that define apnoeas and desaturations, meaning that apnoeas can be identified with those criteria that each physician considers most suitable. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Sleep Apnoea Syndrome (SAS) structural pattern recognition Fuzzy set theory Knowledge representation Constraint satisfaction problems Biosignal processing

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Validation indices for graph clustering

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pattern recognition LETTERS 2003年第8期24卷 1107-1113页

作者： Günter, S Bunke, H Univ Bern Inst Informat & Angew Math Dept Comp Sci CH-3012 Bern Switzerland

In this paper, a new clustering algorithm for the domain of graphs is introduced. Also a number of cluster validation indices are reviewed. These indices aim at finding the optimal number of clusters automatically. The suitability of the considered indices is experimentally evaluated. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： structural pattern recognition graphs clustering graph clustering cluster validation indices self organizing map

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AUTOMATICALLY INFERRED MARKOV NETWORK MODELS FOR CLASSIFICATION OF CHROMOSOMAL BAND pattern STRUCTURES

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CYTOMETRY 1990年第1期11卷 26-39页

作者： GRANUM, E THOMASON, MG AALBORG UNIV INST ELECTR SYST IMAGE ANAL LAB BADEHUSVEJ 23 DK-9000 AALBORG DENMARK UNIV TENNESSEE DEPT COMP SCI KNOXVILLE TN 37996 USA

A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonolinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles". A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise nromalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementation can process one cell in less than 1 min on current standard hardwar

关键词： Chromosome band patterns string representations local sequential features dynamic programming grammatical inference structural pattern recognition

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Self-Driven Algorithm for Solving Supermodular (max, plus ) Labeling Problems Based on Subgradient Descent*

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CYBERNETICS AND SYSTEMS ANALYSIS 2022年第4期58卷 510-517页

作者： Krygin, V Khomenko, R. Natl Acad Sci Ukraine Int Sci Educ Ctr Informat Technol & Syst Kiev Ukraine Minist Educ & Sci Ukraine Kiev Ukraine Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

An algorithm presented in this article gives one of two answers to any (max,+) labeling problem with integer weights given at the input, namely, either a solution in the form of the optimal labeling or the phrase "The problem is not supermodular" with any of these answers guaranteed to be correct. The self-driven properties of the algorithm are in the fact that the user cannot decide, which of the two questions are to be answered, as it is up to the algorithm what is in its area of competence. Another special feature of the algorithm is the fact that it does not need to know the order of labels in the case of supermodular problems. The finite number of steps of the algorithm is guaranteed by using subgradient descent and the integer weights of vertices and edges.

关键词： (max +) labeling problems supermodular labeling problems self-driven pattern recognition discrete optimization graphical models structural pattern recognition

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ABSTRACT CODE NETWORK AS A MODEL OF PERCEPTUAL MEMORY

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pattern recognition 1987年第1期20卷 143-151页

作者： MELLINK, H BUFFART, H UNIV NIJMEGEN PSYCHOL LAB MONTESSORILAAN 3 POB 9104 6500 HE NIJMEGEN NETHERLANDS

According to structural Information Theory human observers represent important aspects of the information of visual patterns in abstract codes. The theory proposes a syntax for the structural form of these codes. In this paper a generative syntax is presented that suitable for the generation of a theory based computer model of the human memory. The model consists of many abstract codes. The memory is assumed to be actively used to interpret new information. Our aim is to simulate this interpretation process. The present development of memory data structures and of processes working upon them is a first step.

关键词： structural information theory Sequential codes Top down process Perceptual learning Associative network Coding theory of pattern perception structural pattern recognition pattern interpretation

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AN ALGEBRAIC APPROACH TO AUTOMATIC CONSTRUCTION OF structural MODELS

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1993年第12期15卷 1298-1311页

作者： NISHIDA, H MORI, S FUKUSHIMA PREFECTURE GOVT UNIV PREPARAT OFF FUKUSHIMA 960 JAPAN

We present an algebraic approach to the inductive learning of structural models and automatic construction of shape prototypes for character recognition on the basis of the algebraic description of curve structure proposed by Nishida and Mori. A class in the structural models is a set of shapes that can be transformed continuously to each other. We consider an algebraic representation of continuous transformation of components of the shape, and give specific properties satisfied by each component in the class. The generalization rules in the inductive learning are specified from the viewpoints of continuous transformation of components and relational structure among the components. The learning procedure generalizes a pair of classes into one class incrementally and hierarchically in terms of the generalization rules. We show experimental results on handwritten numerals.

关键词： CHARACTER recognition INDUCTIVE LEARNING QUALITATIVE VISION SHAPE ANALYSIS structural pattern recognition structural MODEL

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Synthesis of function-described graphs and clustering of attributed graphs

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INTERNATIONAL JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2002年第6期16卷 621-655页

作者： Serratosa, F Alquézar, R Sanfeliu, A Univ Rovira & Virgili Dept Engn Informat & Matemat Tarragona Catalonia Spain Univ Politecn Catalunya Dept Llenguatges & Sistemes Informat ES-08034 Barcelona Spain Univ Politecn Catalunya CSIC Inst Robot & Informat Ind ES-08034 Barcelona Spain

Function-Described Graphs (FDGs) have been introduced by the authors as a representation of an ensemble of Attributed Graphs (AGs) for structural pattern recognition alternative to first-order random graphs. Both optimal and approximate algorithms for error-tolerant graph matching, which use a distance measure between AGs and FDGs, have been reported elsewhere. In this paper, both the supervised and the unsupervised synthesis of FDGs from a set of graphs is addressed. First, two procedures are described to synthesize an FDG from a set of commonly labeled AGs or FDGs, respectively. Then, the unsupervised synthesis of FDGs is studied in the context of clustering a set of AGs and obtaining an FDG model for each cluster. Two algorithms based on incremental and hierarchical clustering, respectively, are proposed, which are parameterized by a graph matching method. Some experimental results both on synthetic data and a real 3D-object recognition application show that the proposed algorithms are effective for clustering a set of AGs and synthesizing the FDGs that describe the classes. Moreover, the synthesized FDGs are shown to be useful for pattern recognition thanks to the distance measure and matching algorithm previously reported.

关键词： attributed graphs clustering probabilistic and structural synthesis error-tolerant graph matching random graphs function-described graphs structural pattern recognition 3D-object recognition

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