Yilmaz C Asli, Kahya Yasemin P
Syst. & Control Eng., Bogazici Univ., Istanbul, Turkey.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:2864-7. doi: 10.1109/IEMBS.2006.259385.
In this study, respiratory sounds of pathological and healthy subjects were analyzed via frequency spectrum and AR model parameters with a view to construct a diagnostic aid based on auscultation. Each subject is represented by 14 channels of respiratory sound data of a single respiration cycle. Two reference libraries, pathological and healthy, were built based on multi-channel respiratory sound data for each channel and for each respiration phase, inspiration and expiration, separately. A multi-channel classification algorithm using K nearest neighbor (k-NN) classification method was designed. Performances of the two classifiers using spectral feature set corresponding to quantile frequencies and 6th order AR model coefficients on inspiration and expiration phases are compared.
在本研究中,通过频谱和自回归(AR)模型参数对病理受试者和健康受试者的呼吸音进行了分析,以期构建一种基于听诊的诊断辅助工具。每个受试者由单个呼吸周期的14个呼吸音数据通道表示。基于多通道呼吸音数据,分别针对每个通道以及吸气和呼气这两个呼吸阶段,构建了病理和健康两个参考库。设计了一种使用K近邻(k-NN)分类方法的多通道分类算法。比较了在吸气和呼气阶段使用对应于分位数频率的频谱特征集和六阶AR模型系数的两种分类器的性能。