Wavelets and fuzzy relational classifiers: A novel spectroscopy analysis system for pediatric metabolic brain diseases

A suspected metabolic disorder presents a difficult challenge to the physician and the patient. We have developed a fully automated system in order to analyze and classify the magnetic resonance spectroscopy signals of patients with metabolic brain diseases. We utilized wavelets to extract signal features and in the time-frequency representations to optimize the feature extraction procedure. Novel fuzzy membership functions and a fuzzy relational classifier were designed to categorize the metabolic brain diseases in children using the information obtained from the feature extraction routine. The sensitivity (Se) and the positive predictivity (PP) of 88.26% and 91.04% in extracting features and 89.66% and 100%, respectively, in detecting metabolic brain diseases has been achieved.

[1]  Richard Wagner.  An overview of computer-assisted diagnostic software , 2005, Metabolic Brain Disease.

[2]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[3]  Arnon D. Cohen,et al.  Biomedical Signal Processing , 1986 .

[4]  Wei-Yen Hsu,et al.  Wavelet-based fractal features with active segment selection: Application to single-trial EEG data , 2007, Journal of Neuroscience Methods.

[5]  Derek A. Linkens,et al.  Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms: Part I. Classification of depth of anaesthesia and development of a patient model , 2005, Artif. Intell. Medicine.

[6]  Wolfgang Rosenstiel,et al.  AFC: ART-Based Fuzzy Classifier , 2003, KES.

[7]  S. Provencher.  Estimation of metabolite concentrations from localized in vivo proton NMR spectra , 1993, Magnetic resonance in medicine.

[8]  George Kalliris,et al.  Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: A bioacoustics application in gastrointestinal-motility monitoring , 2007, Comput. Biol. Medicine.

[9]  J. R. Griffiths.  Clinical applications of MR spectroscopy , 1999 .

[10]  Hong Yan,et al.  Signal processing for magnetic resonance imaging and spectroscopy , 2002 .

[11]  William H. Press,et al.  Numerical recipes in C , 2002 .

[12]  Michio Sugeno,et al.  Fuzzy systems theory and its applications , 1991 .

[13]  Anders la Cour-Harbo,et al.  Ripples in Mathematics , 2003 .

[14]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients , 2005, Journal of Neuroscience Methods.

[15]  Jing-Shing Yao,et al.  Fuzzy decision making for medical diagnosis based on fuzzy number and compositional rule of inference , 2001, Fuzzy Sets Syst..

[16]  Paul S. Addison,et al.  Fractals and Chaos: An Illustrated Course , 1997 .

[17]  S. Qian.  Introduction to Time-Frequency and Wavelet Transforms , 2001 .

[18]  Riza C. Berkan,et al.  Fuzzy System Design Principles , 1997 .

[19]  S. Huffel,et al.  MR spectroscopy quantitation: a review of time‐domain methods , 2001, NMR in biomedicine.

[20]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[21]  Alireza Khotanzad,et al.  Fuzzy classifier design using genetic algorithms , 2007, Pattern Recognit..

[22]  Ujjwal Maulik,et al.  A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification , 2005, Fuzzy Sets Syst..

[23]  Stephen T. Welstead,et al.  Neural network and fuzzy logic applications in C/C++ , 1994, Wiley professional computing.

[24]  Metin Akay,et al.  Time frequency and wavelets in biomedical signal processing , 1998 .

[25]  P. Barker,et al.  In vivo proton MR spectroscopy of the human brain , 2006 .

[26]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook , 2002 .

[27]  Rangaraj M. Rangayyan,et al.  Feature identification in the time-frequency plane by using the Hough-Radon transform , 2001, Pattern Recognit..

[28]  Maysam F. Abbod,et al.  Survey of utilisation of fuzzy technology in Medicine and Healthcare , 2001, Fuzzy Sets Syst..

[29]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[30]  M Filippi,et al.  MR imaging in white matter diseases of the brain and spinal cord , 2005 .

[31]  U. Klose,et al.  MR imaging and localized proton MR spectroscopy in late infantile neuronal ceroid lipofuscinosis. , 1998, AJNR. American journal of neuroradiology.

[32]  S. Cerutti,et al.  A wavelet packets decomposition algorithm for quantification of in vivo (1)H-MRS parameters. , 2002, Medical engineering & physics.

[33]  Gregory W. Wornell,et al.  Estimation of fractal signals from noisy measurements using wavelets , 1992, IEEE Trans. Signal Process..

[34]  A. Ahmadian,et al.  ECG Feature Extraction Based on Multiresolution Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[35]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[36]  P. Babyn,et al.  ECG Arrhythmia Detection Using Fuzzy Classifiers , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[37]  Carles Arús,et al.  Automated classification of short echo time in in vivo 1H brain tumor spectra: A multicenter study , 2003, Magnetic resonance in medicine.

[38]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[39]  Amara Lynn Graps,et al.  An introduction to wavelets , 1995 .

[40]  P. Babyn,et al.  Bio-signal Characteristics Detection Utilizing Frequency Ordered Wavelet Packets , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[41]  J. Valk,et al.  Magnetic Resonance of Myelination and Myelin Disorders , 1989 .

[42]  R. Sener.  The glycine peak in brain diseases. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.