Feature extraction through discrete wavelet transform coefficients

Discrete wavelet transform has become a widely used feature extraction tool in pattern recognition and pattern classification applications. However, using all wavelet coefficients as features is not desirable in most applications -- the enormity of data and irrelevant wavelet coefficients may adversely affect the performance. Therefore, this paper presents a novel feature extraction method based on discrete wavelet transform. In this method, Shannon's entropy measure is used for identifying competent wavelet coefficients. The features are formed by calculating the energy of coefficients clustered around the competent clusters. The method is applied to the lung sound classification problem. The experimental results show that the new method performs better than a well-known feature extraction method that is known to give the best results for lung sound classification problem.

[1]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[2]  Ching-Tang Hsieh,et al.  Robust speech features based on wavelet transform with application to speaker identification , 2002 .

[3]  A. Chan,et al.  Automatic feature extraction from wavelet coefficients using genetic algorithms , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[4]  Ashraf A. Kassim,et al.  Application of image and sound analysis techniques to monitor the condition of cutting tools , 2000, Pattern Recognit. Lett..

[5]  Jun Zheng,et al.  Wavelet based feature reduction method for effective classification of hyperspectral data , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[6]  Jiang Li,et al.  Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[7]  Pranab Kumar Sen,et al.  Large Sample Methods in Statistics: An Introduction with Applications , 1993 .

[8]  L. Robertsson,et al.  Analyzing bacteriological growth using wavelet transform , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[9]  Zwe-Lee Gaing,et al.  Implementation of power disturbance classifier using wavelet-based neural networks , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[10]  Ibrahim N. Tansel,et al.  Tool wear estimation in micro-machining.: Part II: neural-network-based periodic inspector for non-metals , 2000 .

[11]  Y. S. Tarng,et al.  Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current , .

[12]  Sang Won Nam,et al.  Efficient feature vector extraction for automatic classification of power quality disturbances , 1998 .

[13]  J. Morlet,et al.  Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media , 1982 .

[14]  Yan Huang,et al.  Using wavelet transform of hyperspectral reflectance curves for automated monitoring of Imperata cylindrica (cogongrass) , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[15]  Mostefa Mesbah,et al.  An optimal feature set for seizure detection systems for newborn EEG signals , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[16]  Y. Ueno,et al.  Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals , 1996 .

[17]  J. S. Sahambi,et al.  Classification of ECG arrhythmias using multi-resolution analysis and neural networks , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[18]  Lori M. Bruce,et al.  Using neural networks with wavelet transforms for an automated mammographic mass classifier , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[19]  Ioannis Minis,et al.  WAVELET BASED CUTTING STATE IDENTIFICATION , 1998 .

[20]  Jiang Li,et al.  Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .

[21]  Y. S. Tarng,et al.  Milling cutter breakage detection by the discretewavelet transform fn1 fn1 This paper has not beenpu , 1999 .

[22]  Tamer Ölmez,et al.  Determination of features for heart sounds by using wavelet transforms , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

[23]  Paul Scheunders,et al.  Wavelet-based feature extraction for hyperspectral vegetation monitoring , 2004, SPIE Remote Sensing.

[24]  Weaam Alkhaldi,et al.  Multi-band based recognition of spoken Arabic numerals using wavelet transform , 2002, Proceedings of the Nineteenth National Radio Science Conference.

[25]  N. Younan,et al.  Hyperspectral soil texture classification , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[26]  Witold Kinsner,et al.  Power system transient modelling and classification , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[27]  Fritz Klocke,et al.  Application of a wavelet-based signal analysis for evaluating the tool state in cutting operations , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[28]  J. G. Williams,et al.  Wavelet transform of reactor power transients , 1994, Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94.

[29]  Wang Zhong.  Monitoring Tool Wear States in Turning Based on Wavelet Analysis , 2001 .

[30]  Hojjat Adeli,et al.  Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis , 2000 .

[31]  Sagar V. Kamarthi,et al.  Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Naresh Sarwabhotla,et al.  Improved radar object identification using wavelet transform and ART-2 neural network , 2003, Proceedings EC-VIP-MC 2003. 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications (IEEE Cat. No.03EX667).

[33]  Mostefa Mesbah,et al.  Detection of newborn EEG seizure using optimal features based on discrete wavelet transform , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[34]  A. Y. Chikhani,et al.  Genetic Algorithms Based Economic Dispatch for Cogeneration Units Considering Multiplant , 2022 .

[35]  J. Morlet,et al.  Wave propagation and sampling theory—Part II: Sampling theory and complex waves , 1982 .