【摘要】 基于自适应向量机监测睡眠呼吸暂停综合征(SAS)时可提取出的特征参数较多,筛选这些特征参数中与SAS相关度较大的组合,可以有效降低算法的计算量,具有重要的实践意义。本文基于V2导联心电信号,首先对ECG信号进行去噪和R波提取,得到心率变异性信号(HRV)和心电呼吸导出信号,并从中提取出时域频域特征共22组,利用特征参数与SAS的相关系数对特征参数筛选后进行支持向量机(SVM)分类。对比22组特征参数与筛选后的15组特征参数分类结果,准确率降低不足0.5%,但计算复杂度大大降低,可作为对临床长时间心电图检测的扩展,减少对专业医护人员的依赖,具有良好的经济性和普及性。更多还原

【Abstract】 There are so much characteristic parameters can be extracted based on adaptive vector machine for detecting sleep apnea syndrome. It has important significance which is selected the characteristic parameters to reduce the amounts of calculation applied in sleep apnea syndrome. This study adopted the electrocardiogram signals from limb guided lead-Ⅱ and then denoised the signal interference and detected the R-wave to get the heart rate variability data and ECG-derived respiratory data. Analysis these two data that we can obtain the twenty-two features in time domain and frequency domain, moreover, using support vector machines algorithm to classify the sleep apnea syndrome feature parameters. Compared the twenty-two features with optimal fifteen feature parameters we proposed, the amounts of calculation are decrease obviously without decay the classification accuracy. It can be used as an extension of clinical long time electrocardiogram detection because it can reduce the dependence on health care professional. Therefore, it has good economy and popularity.更多还原