A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier

In this study, an intelligent system based on genetic-support vector machines (GSVM) approach is presented for classification of the Doppler signals of the heart valve diseases. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound. GSVM is used in this study for diagnosis of the heart valve diseases. The GSVM selects of most appropriate wavelet filter type for problem, wavelet entropy parameter, the optimal kernel function type, kernel function parameter, and soft margin constant C penalty parameter of support vector machines (SVM) classifier. The performance of the GSVM system proposed in this study is evaluated in 215 samples. The test results show that this GSVM system is effective to detect Doppler heart sounds. The averaged rate of correct classification rate was about 95%.

[1]  I. Guler,et al.  Detection of mitral stenosis by a pulsed Doppler flowmeter and autoregressive spectral analysis method , 1995, Proceedings of the First Regional Conference, IEEE Engineering in Medicine and Biology Society and 14th Conference of the Biomedical Engineering Society of India. An International Meet.

[2]  Bernhard Schölkopf,et al.  Statistical Learning and Kernel Methods , 2001, Data Fusion and Perception.

[3]  H. Liang,et al.  A feature extraction algorithm based on wavelet packet decomposition for heart sound signals , 1998, Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380).

[4]  Engin Avci,et al.  A new optimum feature extraction and classification method for speaker recognition: GWPNN , 2007, Expert Syst. Appl..

[5]  José A. Romagnoli,et al.  Process data de-noising using wavelet transform , 1999, Intell. Data Anal..

[6]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[7]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[8]  Engin Avci,et al.  Intelligent target recognition based on wavelet packet neural network , 2005, Expert Syst. Appl..

[9]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[10]  Engin Avci,et al.  An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification , 2007, Expert Syst. Appl..

[11]  M. Graça Ruano,et al.  Real-time implementation of a Doppler signal spectral estimator using sequential and parallel processing techniques , 2000, Microprocess. Microsystems.

[12]  Suresh R. Devasahayam,et al.  Signals and systems in biomedical engineering , 2000 .

[13]  Bernhard Schölkopf,et al.  Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[14]  Fernando S. Schlindwein,et al.  Application of wavelets in Doppler ultrasound , 1997 .

[15]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[16]  Vincent Baeten,et al.  Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .

[17]  E. Karabetsos,et al.  Design and development of a new ultrasonic doppler technique for estimation of the aggregation of red blood cells , 1998 .

[18]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[19]  Emre Çomak,et al.  A decision support system based on support vector machines for diagnosis of the heart valve diseases , 2007, Comput. Biol. Medicine.

[20]  H. Dickhaus,et al.  Classifying biosignals with wavelet networks [a method for noninvasive diagnosis] , 1996 .

[21]  Metin Akay,et al.  Neural networks for the diagnosis of coronary artery disease , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[22]  Ahmet Arslan,et al.  A wavelet neural network for the detection of heart valve diseases , 2003, Expert Syst. J. Knowl. Eng..

[23]  Melani Plett.  Ultrasonic arterial vibrometry with wavelet based detection and estimation , 2000 .

[24]  F. K. Lam,et al.  Fast detection of venous air embolism in Doppler heart sound using the wavelet transform , 1997, IEEE Transactions on Biomedical Engineering.

[25]  Jos R.T.C. Roelandt,et al.  Doppler ultrasound in cardiology: Physical principles and clinical applications: Liv Hatle, Bjorn Angelson Lea & Febiger, Philadelphia; 1982; £23.00 , 1984 .

[26]  R Quian Quiroga,et al.  Wavelet entropy: a measure of order in evoked potentials. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[27]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[28]  N A Gough,et al.  Neural network analysis of Doppler ultrasound blood flow signals: a pilot study. , 1997, Ultrasound in medicine & biology.

[29]  Majid Mirmehdi,et al.  Comparative Exudate Classification Using Support Vector Machines and Neural Networks , 2002, MICCAI.

[30]  Liu Wei,et al.  Noninvasive acoustical analysis system of coronary heart disease , 1997, Proceedings of the 1997 16 Southern Biomedical Engineering Conference.