中医闻诊信号采集与分析在虚、实证型辨别中的应用（英文）

目的：通过采集和分析声音信号,提取特征参数,为中医虚、实证型的辨别提供客观依据。方法：运用＂中医闻诊采集系统＂采集声音样本共308例,其中气虚150例,阴虚52例,实证55例,正常51例。运用小波包变换结合近似熵的非线性方法分析处理各组声音样本。提取与中医虚实辨证相关的特征参数。将特征向量输入支持向量机分类器得出证型识别的准确率。结果：小波包分解各组样本得到不同频段的近似熵数据,经统计分析得到：正常与非健康比较,近似熵值在2～3kHz、3.5～4kHz和5～8kHz频率段的多个频率节点有显著性差异（P≤0.05）;实证与虚证比较,近似熵值在0～2.5kHz和6～6.5kHz频率段的多个频率节点有显著性差异（P≤0.05）;气虚与阴虚比较,近似熵值在2～4kHz对应的频率节点有显著性差异（P≤0.05）。各组分类识别结果表明,正常与非健康、实证与虚证、气虚与阴虚各组的识别准确率均达到较高水平。结论：现代语音信号采集分析方法为中医虚、实证型的辨别提供了一定的客观参考依据。

Application of voice signal collection and analysis in traditional Chinese medicine syndrome differentiation of deficiency and excess

Objective:To provide more objective basis for traditional Chinese medicine(TCM)syndrome differentiation of deficiency and excess by collecting and analyzing voice signals and extracting characteristic parameters.Methods:All of 308 samples including 150 samples of qi-deficiency,52 yin-deficiency,55 excess and 51 normal were collected by"Voice Collecting System of TCM"and analyzed by wavelet packet transform(WPT)combined with approximate entropy(ApEn).The characteristic parameters with remarkable differences were chosen as input vectors for support vector machine(SVM)Classifier to obtain classification results.Results:Comparison between the normal and non-healthy showed that ApEn values of several nodes in 2 to3 kHz,3.5 to 4 kHz and 5 to 8 kHz frequencies were significantly different(P≤0.05);comparison between the deficiency and excess showed that ApEn values of several nodes in 0 to 2.5 kHz and 6 to 6.5 kHz frequencies were significantly different(P≤0.05);comparison between the qi-deficiency and yin-deficiency showed that ApEn values of node in 2 to 4 kHz frequency were significantly different(P≤0.05).The outputs of SVM showed that accuracies of samples in each group had good classification results by analyzing the ApEn values in different frequencies through WPT.Conclusion:The methods of voice collection and analysis used in the auscultation of TCM can provide objective basis for syndrome differentiation of deficiency and excess.