A fuzzy constraint satisfaction approach for signal abstraction

We present the Multivariable Fuzzy Temporal Profile model (MFTP), a formal model which allows us to represent signal patterns and identify their occurrences over the temporal evolution of a set of physical parameters. The pattern comprises a set of findings, each one of which may, in turn, be a pattern, so that its recognition is organized into a hierarchy of abstraction levels, and ultimately they are associated with the appearance of certain distinctive morphologies - profiles - over each parameter. The patterns definition is obtained directly from humans experts, either with the help of a formal language or with a visual tool developed for this purpose. The model is based, on the one hand, on Fuzzy Set Theory, which allows the vagueness and imprecision which are characteristic of human knowledge to be modelled; and on the other hand, on the formalism of Constraint Satisfaction Problems (CSP), in order to obtain a representation capable of explicitly capturing the hierarchy of abstraction levels into which the recognition task is organized. We supply algorithms for analyzing the consistency of the information defined by the MFTP, and for the recognition of patterns over signal recordings.

[1]  D. Dubois,et al.  Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions , 1999 .

[2]  G. Passariello,et al.  Intelligent patient monitoring and management systems: a review , 1993, IEEE Engineering in Medicine and Biology Magazine.

[3]  Didier Dubois,et al.  Possibility theory , 2018, Scholarpedia.

[4]  Yuval Shahar,et al.  A Framework for Knowledge-Based Temporal Abstraction , 1997, Artif. Intell..

[5]  Harrell Smith,et al.  TRACE , 1981, The Garden in Winter and Other Poems.

[6]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[7]  Rina Dechter,et al.  Constraint Processing , 1995, Lecture Notes in Computer Science.

[8]  Evangelos E. Milios,et al.  Signal abstractions in signal processing software , 1989, IEEE Trans. Acoust. Speech Signal Process..

[9]  Roque Marín,et al.  Modeling the representation of time from a fuzzy perspective , 1994 .

[10]  Richard W. Jones,et al.  Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns , 1999, Artif. Intell. Medicine.

[11]  Rina Dechter,et al.  Temporal Constraint Networks , 1989, Artif. Intell..

[12]  Yuval Shahar,et al.  Knowledge-Based Spatiotemporal Abstraction , 1996 .

[13]  William S. Havens,et al.  Hierarchical arc consistency: exploiting structured domains in constraint satisfaction problems , 1985 .

[14]  Michel Grabisch,et al.  Temporal scenario modelling and recognition based on possibilistic logic , 2003, Artif. Intell..

[15]  Senén Barro,et al.  TRACE, a graphical tool for the acquisition and detection of signal patterns , 2009, Expert Syst. Appl..

[16]  Pedro Melo-Pinto,et al.  Systematic Organisation of Information in Fuzzy Systems , 2003 .

[17]  G. Stephanopoulos,et al.  Representation of process trends—Part I. A formal representation framework , 1990 .

[18]  Eugene C. Freuder.  A Sufficient Condition for Backtrack-Free Search , 1982, JACM.

[19]  Senén Barro,et al.  Fuzzy constraint satisfaction approach for landmark recognition in mobile robotics , 2006, AI Commun..

[20]  Toshiomi Yoshida,et al.  Real-time qualitative analysis of the temporal shapes of (bio) process variables , 1992 .

[21]  Michel Dojat,et al.  Scenario recognition for temporal reasoning in medical domains , 1998, Artif. Intell. Medicine.

[22]  C.O.S. Sorzano,et al.  Intelligent alarms for patient supervision , 2007, 2007 IEEE International Symposium on Intelligent Signal Processing.

[23]  Barbara Hayes-Roth,et al.  tFPR: A fuzzy and structural pattern recognition system of multi-variate time-dependent pattern classes based on sigmoidal functions , 1998, Fuzzy Sets Syst..

[24]  Friedrich Steimann.  The interpretation of time-varying data with DIAMON-1 , 1996, Artif. Intell. Medicine.

[25]  Alberto Riva,et al.  Temporal Abstractions for Interpreting Diabetic Patients Monitoring Data , 1998, Intell. Data Anal..

[26]  Senén Barro,et al.  Fuzzy constraint networks for signal pattern recognition , 2003, Artif. Intell..

[27]  Madan M. Gupta,et al.  Introduction to Fuzzy Arithmetic , 1991 .

[28]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[29]  LangJérôme,et al.  Fuzzy sets in approximate reasoning, part 2 , 1991 .