喘鸣音的时频谱图特征提取与信号检测  

Spectrum Feature Extraction and Signal Detection of Wheeze in Time-frequency domain

作　　者：李真真[1] 吴效明[1] 

机构地区：[1]华南理工大学生物科学与工程学院,广州510006

出　　处：《信号处理》2013年第4期429-435,共7页Journal of Signal Processing

基　　金：国家自然科学基金资助项目(81070612)

摘　　要：文中提出了一种基于S变换时频谱图的喘鸣音信号检测算法。喘鸣音在时域中具有类似正弦波的形态,但难以直接提取特征。S变换在高频处具有较高的时间分辨率,在低频处具有较高的频率分辨率,可精细化分析喘鸣音信号时频特征。文中通过对呼吸音信号做S变换生成对应的时频谱图,提取与喘鸣音对应的二维谱图像特征,实现了喘鸣音信号检测。实验表明该算法对单个体自身训练的情形检测效果理想,检测敏感性指标可达100%,正阳性预测值可达98%以上。但对于喘鸣音共性特征提取欠缺,有待进一步探索。We propose a new algorithm to detect wheeze signals in respiratory sounds based on S transform time-frequency spectrum analysis. Wheezes are of sinusoidal morphological characteristics in the time domain, and its features are diffi- cult to extract directly. This paper introduces S transform, which shows high time analysis resolution in high frequency field and high frequency analysis resolution in low frequency field, to extract features of wheezes in the time-frequency domain. Respiratory sounds were transformed to time-frequency domain by S transform, and then, two-dimension spectrum image features, which are corresponding to wheeze signals, were extracted. Thus, wheeze signal detection has been realized. Ex- periments show that the algorithm do well in the case of training and detection for each subject, with a sensitivity value as high as 100% for detection and position prediction value higher than 98%. However, the method failed to extract global features of wheeze from different subjects, which requires future exploratory research.

关 键 词：喘鸣音信号检测 呼吸音信号检测 S变换 时频分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]