COMPUTER-AIDED DIAGNOSIS OF THE PAROXYSMAL ATRIAL FIBRILLATION: A FUZZY-EVOLUTIONARY APPROACH

This paper presents an integrated fuzzy-evolutionary methodology to address computer-aided diagnosis in medical applications by using features extracted from biosignal processing. In the proposed methodology, a deterministic crowding genetic optimizer designed to provide high-diversity solutions is used for weighted feature selection, and the diagnostic decision is made by a binary k-nearest neighbor classifier. Weight vector solutions resulting from the optimization stage are processed by a fuzzy rules generator to retrieve a fuzzy model giving friendly information to the medical specialist about the role of the different features in the diagnosis. This allows the design of efficient diagnosis protocols. The overall diagnostic methodology is applied to Paroxysmal Atrial Fibrillation detection based on analysis of nonfibrillation ECGs, obtaining a fuzzy model consistent with previous work in this field.

[1]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Julio Ortega Lopera,et al.  Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes , 2006, IEEE Transactions on Biomedical Engineering.

[4]  P F Angelino,et al.  [Computers in cardiology]. , 1980, Minerva medica.

[5]  C. M. Hill.  Computer-aided diagnosis. , 1992, Dental update.

[6]  P. Petersen,et al.  Embolic complications in paroxysmal atrial fibrillation. , 1986, Stroke.

[7]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[8]  W. Peizhuang.  Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[9]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[10]  David J. Hand,et al.  Discrimination and Classification , 1982 .

[11]  P. John Clarkson,et al.  A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2002, Evolutionary Computation.

[12]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[13]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[14]  F. J. Fernández,et al.  ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm , 2004, Comput. Biol. Medicine.

[15]  John F. Kolen,et al.  Reducing the time complexity of the fuzzy c-means algorithm , 2002, IEEE Trans. Fuzzy Syst..

[16]  In Schoenauer,et al.  Parallel Problem Solving from Nature , 1990, Lecture Notes in Computer Science.

[17]  D B Fogel,et al.  Evolving neural networks for detecting breast cancer. , 1995, Cancer letters.

[18]  Jiawei Han,et al.  Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.

[19]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[20]  Frank Klawonn,et al.  A contribution to convergence theory of fuzzy c-means and derivatives , 2003, IEEE Trans. Fuzzy Syst..

[21]  E Ros,et al.  Automatic Paroxysmal Atrial Fibrillation Based on not Fibrillating ECGs , 2004, Methods of Information in Medicine.

[22]  Frank Klawonn,et al.  Fuzzy clustering with weighting of data variables , 2000, EUSFLAT-ESTYLF Joint Conf..

[23]  Erick Cantú-Paz,et al.  Feature Subset Selection by Estimation of Distribution Algorithms , 2002, GECCO.

[24]  中澤 真,et al.  Devroye, L., Gyorfi, L. and Lugosi, G. : A Probabilistic Theory of Pattern Recognition, Springer (1996). , 1997 .

[25]  Lakhmi C. Jain,et al.  Fuzzy clustering models and applications , 1997, Studies in Fuzziness and Soft Computing.

[26]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[27]  Qiaoping Zhang,et al.  A New and Efficient K-Medoid Algorithm for Spatial Clustering , 2005, ICCSA.

[28]  Samir W. Mahfoud.  A Comparison of Parallel and Sequential Niching Methods , 1995, ICGA.

[29]  Robert M. Nishikawa,et al.  Optimization and FROC analysis of rule-based detection schemes using a multiobjective approach , 1998, IEEE Transactions on Medical Imaging.

[30]  Jeffrey C. Bauer,et al.  Telemedicine and the reinvention of healthcare , 1999 .

[31]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

[32]  Thomas Roß,et al.  Feature selection for optimized skin tumor recognition using genetic algorithms , 1999, Artif. Intell. Medicine.