Wavelet packet-based identification of complex oscillation in biological signals  

作　　者：Zhang Shuqing Sarah K. Spurgeon Zhang Liguo Jin Mei John A. Twiddle Femando S. Schlindwein 

机构地区：[1]College of Electric Engineering, Yanshan University, Qinhuangdao 066004, China [2]Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK

出　　处：《仪器仪表学报》2008年第2期225-232,共8页Chinese Journal of Scientific Instrument

基　　金：Supported by the National Natural Science Foundation of China (60102002);the Doctoral Foundation of Hebei Province of China(B2004522)

摘　　要：Owing to the intrinsic nonlinearities of the system,a contracting mechanism,such as myogenic response,may induce different oscillatory patterns.Many specialists discussed the relations of oscillatory patterns with intrinsic control system or some pathological condition,but there is no single,well-defined criterion to achieve the identification of regular,stochastic,and chaotic activities.In this paper,we focus on the Mallat algorithm of wavelet packet and use it in the identification of the regular periodic,stochastic,and chaotic fluctuations.According to the specific frequency configuration of the chaos activity,we select proper layers of decomposition of wavelet packet and did fine segments to the frequency of signals.The frequency band of energy convergence could be recognized.The signal of periodic,stochastic,and chaotic could be distinguished depending on it.Numerical experiment is given to show its efficiency.Experiments on 12 babies' lung data have been done.This identification by means of wavelet packet could support the cardiologist or cerebral specialist to do more observation and deeper analysis to physic signals.Owing to the intrinsic nonlinearities of the system, a contracting mechanism, such as myogenic response, may induce different oscillatory patterns. Many specialists discussed the relations of oscillatory patterns with intrinsic control system or some pathological condition, but there is no single, well-defined criterion to achieve the identification of regular, stochastic, and chaotic activities. In this paper, we focus on the Mallat algorithm of wavelet packet and use it in the identification of the regular periodic, stochastic, and chaotic fluctuations. According to the specific frequency configuration of the chaos activity, we select proper layers of decomposition of wavelet packet and did fine segments to the frequency of signals. The frequency band of energy convergence could be recognized. The signal of periodic, stochastic, and chaotic could be distinguished depending on it. Numerical experiment is given to show its efficiency. Experiments on 12 babies＇ lung data have been done. This identification by means of wavelet packet could support the cardiologist or cerebral specialist to do more observation and deeper analysis to physic signals.

关 键 词：复振荡 生物信号 鉴别模式 不规则波动 微波 

分 类 号：Q811.4[生物学—生物工程]