基于小波变换的时频图建模及其在嗅觉场电位分析中的应用  

A Wavelet-based Time-frequency Modeling Method and Its Application in Analysis of Local Field Potentials in Olfactory Bulb

作　　者：董琪[1] 胡靓[1] 庄柳静[1] 周俊[1] 王平[1] 

机构地区：[1]浙江大学生物医学工程与仪器学院生物传感器国家专业实验室生物医学工程教育部重点实验室,杭州310027

出　　处：《生物医学工程学杂志》2014年第3期481-486,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(81027003);教育部博士点基金资助项目(20120101130011)

摘　　要：快速有效地提取和比较不同神经信号中所含信息的相同和区别之处是研究者关注的问题。本文介绍了一种基于小波变换的时频建模方法。该方法用少量的半椭球模型来表征神经信号在时域和频域上的变化,克服了传统时频分析中背景干扰大、参数多的缺陷。将该方法应用于嗅球场电位的研究,与支持向量机(SVM)算法相结合,可初步实现气味的分类。The study of neuronal activity with low frequency has shown an increasing interest for its greater stability and reliability recent years. One challenge in analyzing this kind of activity is to find similarities and differences be- tween signals efficiently and effectively. The traditional analysis methods, such as short-time Fourier transform, are easily obscured by background noises and often involve a large number of parameters. Therefore, this paper intro- duces a novel time-frequency analysis method based on wavelet transformation and half-ellipsoid modeling to extract instantaneous frequency and instantaneous phase information. This method overcomes some shortcomings of conven- tional time-frequency analysis. In this method, wavelet transformation is used to provide high-level representations of raw signals, and parsimonious half-ellipsoid models are used to extract changes in time domain and frequency domain of neural recordings. The method was validated to local field potentials （LFPs） of olfactory bulb of anesthetized rats during three different odor stimuli. The results suggested that this method could detect odor-relevant features from olfactory signals with large variability. The Odors then were classified with support vector machine （SVM） algo- rithm and the classification accuracy reached 79.4%.

关 键 词：时频分析 局部场电位 嗅球 气味识别 小波变换 

分 类 号：TN911.7[电子电信—通信与信息系统]