Title :
Optimal wavelet packet decomposition for rectal pressure signal feature extraction
Author :
Enyu Jiang ; Peng Zan ; Suqin Zhang ; Xiaojin Zhu ; Yong Shao
Author_Institution :
Shanghai Key Lab. of Power Station Autom. Technol., Shanghai Univ., Shanghai, China
Abstract :
The method of optimal wavelet packet decomposition is proposed for rectal pressure signal feature extraction. By using wavelet packet algorithm, the mean wavelet coefficients and its corresponding energy component with high separability are selected as the feature vector according to the maximum separation degree of Fisher index, and the optimal features vector have specific sub-band wavelet packet coefficients and energy with higher separability. By comparison of the classification result and the operation time of optimized and non-optimized features vectors, the experimental results give the evidence that the proposed method is effective.
Keywords :
medical signal processing; signal classification; wavelet transforms; Fisher index; classification result; energy component; feature vector; maximum separation degree; mean wavelet coefficients; nonoptimized feature vectors; optimal features vector; optimal wavelet packet decomposition; rectal pressure signal feature extraction; subband wavelet packet coefficients; Feature extraction; Support vector machine classification; Vectors; Wavelet analysis; Wavelet packets;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463367