A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals

Computer Methods and Programs in Biomedicine

Volumn 107, Issue 3, 2012, Pages 598-609

  Uĝuz, Harun ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Discrete wavelet transform; Feature selection; Information gain; Principal component analysis; Support vector machine

Indexed keywords

BLOOD FLOW VELOCITY; BRAIN DISEASE; CLASSIFICATION EFFICIENCY; DIMENSION REDUCTION; DIMENSION REDUCTION METHOD; FEATURE RANKING; FEATURE SPACE; HEALTHY PEOPLE; HYBRID FEATURES; INFORMATION GAIN; PCA METHOD; TRANSCRANIAL DOPPLER;

DIAGNOSIS; DISCRETE WAVELET TRANSFORMS; FEATURE EXTRACTION; HYBRID SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; RESEARCH LABORATORIES;

SUPPORT VECTOR MACHINES;

ACCURACY; ARTICLE; BRAIN ARTERY ANEURYSM; BRAIN EDEMA; BRAIN HEMORRHAGE; BRAIN REGION; BRAIN TUMOR; CONTROLLED STUDY; DOPPLER ECHOGRAPHY; HUMAN; HYBRID; INFORMATION GAIN; INFORMATION SYSTEM; MAJOR CLINICAL STUDY; PRINCIPAL COMPONENT ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

ADOLESCENT; ADULT; AGED; BRAIN DISEASES; CHILD; CHILD, PRESCHOOL; COMPUTER SIMULATION; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; MIDDLE AGED; MODELS, STATISTICAL; MODELS, THEORETICAL; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES; ULTRASONOGRAPHY, DOPPLER, TRANSCRANIAL;

EID: 84863882582     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.03.013     Document Type: Article

References (49)

1. Wright I.A., Gough N.A.J., Rakebrandt F., Wahab M., Woodcock J.P. Neural network analysis of Doppler ultrasound blood flow signals: a pilot study. Ultrasound in Medicine and Biology 1997, 23(5):683-690.
2. Miranda P., Lagares A., Alen J., Perez A., Arrese I., Lobato R.D. Early transcranial Doppler after subarachnoid hemorrhage: clinical and radiological correlations. Surgical Neurology 2006, 65:247-252.
3. Ozturk A., Arslan A., Hardalac F. Comparison of neuro-fuzzy systems for classification of transcranial Doppler signals with their chaotic invariant measures. Expert Systems with Applications 2008, 34:1044-1055.
4. Serhatlioĝlu S., Hardalaç F., Güler I. Classification of transcranial Doppler signals using artificial neural network. Journal of Medical Systems 2003, 27(2):205-214.
5. Guler I., Hardalac F., Kaymaz M. Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels. Computers in Biology and Medicine 2002, 32:445-453.
6. Uĝuz H., öztürk A., Saraçoĝlu R., Arslan A. A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases. Expert Systems with Applications 2008, 35(3):1104-1114.
7. Uĝuz H., Arslan A. A new approach based on discrete hidden Markov model using Rocchio algorithm for the diagnosis of the brain diseases. Digital Signal Processing 2010, 20(3):923-934.
8. Graps A. An introduction to wavelets. IEEE Computational Sciences and Engineering 1995, 2(2):50-61.
9. Güler I., übeyli E.D. ECG beat classifier designed by combined neural network model. Pattern Recognition 2005, 38:199-208.
10. Bruni V., Vitulano D. Combined image compression and denoising using wavelets. Signal Processing: Image Communication 2007, 22(1):86-101.
11. Chen J., Zhang Y., Shi X. Image coding based on wavelet transform and uniform scalar dead zone quantizer. Signal Processing: Image Communication 2006, 21(7):562-572.
12. Wu J.-D., Hsu C.-C. Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference. Expert Systems with Applications 2009, 36(2):3785-3794.
13. Ekici S., Yildirim S., Poyraz M. Energy and entropy-based feature extraction for location fault on transmission lines by using neural network and wavelet packet decomposition. Expert Systems with Applications 2008, 34:2937-2944.
14. Gaing X.L. Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 2004, 19:1560-1568.
15. Turkoglu I., Arslan A., Ilkay E. An expert system for diagnosis of the heart valve diseases. Expert Systems with Applications 2002, 23:229-236.
16. Turkoglu I., Arslan A., Ilkay E. An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural Networks. Computers in Biology and Medicine 2003, 33:319-331.
17. Übeyli E.D. Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing 2009, 19(2):297-308.
18. Babaei S., Geranmayeh A. Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCGs signal. Computers in Biology and Medicine 2009, 39(1):8-15.
19. Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 2007, 32(4):1084-1093.
20. Forman G. An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research 2003, 3:1289-1305.
21. Li Y., Hsu D.F., Chung S.M. Combining multiple feature selection methods for text categorization by using rank-score characteristics. 21st IEEE International Conference on Tools with Artificial Intelligence 2009, 508-517.
22. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
23. Vapnik V.N. Statistical Learning Theory 1998, Wiley, New York.
24. Camastra F. A SVM-based cursive character recognizer. Pattern Recognition 2007, 40(12):3721-3727.
25. Chou C.H., Lin C.C., Liu Y.H., Chang F. A prototype classification method and its use in a hybrid solution for multiclass pattern recognition. Pattern Recognition 2006, 39(4):624-634.
26. Ganyun L.V., Cheng H., Zhai H., Dong L. Fault diagnosis of power transformer based on multi-layer SVM classifier. Electric Power Systems Research 2005, 74(1):1-7.
27. Huang S.-C. Online option price forecasting by using unscented Kalman filters and support vector machines. Expert Systems with Applications 2008, 34(4):2819-2825.
28. Zhang J., Wang Y., Dong Y., Wang Y. Ultrasonographic feature selection and pattern classification for cervical lymph nodes using support vector machines. Computer Methods and Programs in Biomedicine 2007, 88(1):75-84.
29. Übeyli E.D. Combining eigenvector methods and support vector machines for detecting variability of Doppler ultrasound signals. Computer Methods and Programs in Biomedicine 2007, 86(2):181-190.
30. Maglogiannis I., Loukis E., Zafiropoulos E., Stasis A. Support Vectors Machine-based identification of heart valve diseases using heart sounds. Computer Methods and Programs in Biomedicine 2009, 95(1):47-61.
31. Cerqueira A.S., Ferreira D.D., Ribeiro M.V., Duque C.A. Power quality events recognition using a SVM-based method. Electric Power Systems Research 2008, 78(9):1546-1552.
32. Wu J.-D., Kuo J.-M. An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network. Expert Systems with Applications 2009, 36(6):9776-9783.
33. Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications 2009, 36:2027-2036.
34. Purushothama V., Narayanana S., Suryanarayana A.N.P. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT&E International 2005, 1-11.
35. Mallat S.G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Anal Machine Intelligence 1989, 11(7):674-693.
36. Faust O., Acharya R.U., Allen A.R., Lin C.M. Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques. IRBM 2008, 29(1):44-52.
37. Kay S.M. Modern Spectral Estimation: Theory and Application 1988, Prentice-Hall, New Jersey.
38. Kullback S. An application of information theory to multivariate analysis. Annals of Mathematical Statistics 1952, 23:88.
39. Deng J.D., Simmermacher C., Cranefield S. A study on feature analysis for musical instrument classification. IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 2008, 38(2):429-438.
40. Zhang Y.X. Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta 2007, 73:68-75.
41. Jolliffe T. Principal Component Analysis, ACM Computing Surveys 1986, Springer-Verlag, pp. 1-47.
42. Kandaswamy A., Kumar C., Ramanathan R., Jayaraman S., Malmurugan N. Neural classification of lung sounds using wavelet coefficients. Computers in Biology and Medicine 2004, 34(6):523-537.
43. Kiymik M.K., Akin M., Subasi A. Automatic recognition of alertness level by using wavelet transform and artificial neural network. Journal of Neuroscience Methods 2004, 139:231-240.
44. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974, 19:716-723.
45. Ferr L. Selection of components in principal component analysis: a comparison of methods. Computational Statistics and Data Analysis 1995, 19:669-682.
46. Valle S., Li W., Qin S.J. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Industrial and Engineering Chemistry Research 1999, 38:4389-4401.
47. Awad M., Motai Y. Dynamic classification for video stream using support vector machine. Applied Soft Computing 2008, 8:1314-1325.
48. Centor R.M. Signal detectability use of ROC curves and their analysis. Medical Decision Making 1991, 102-106.
49. Hanley J.A., McNeil B.J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983, 148(3):839-843.