基于小波变换和粗糙集的早搏信号识别算法

Premature Beat Signal Recognition Algorithm Based on Wavelet Transform and Rough Set

心电特征参数的选择和提取是心电图(ECG)分析的基础,提升检测算法的识别率和特征分类的精度是自动分析技术的关键。提出了基于小波变换和属性约简的心电早搏信号识别算法。该算法首先依据心血管专家的诊断标准选择了12个心电特征参数;然后运用基于小波变换的特征检测算法进行了特征提取,并利用基于粒计算的属性约简算法对特征参数进行了属性约简;最后,将约简后的数据用于模式分类并通过MIT-BIH数据库对结果进行验证。实验表明,约简后的分类精度大大高于约简前的数据,特征参数的合理选择(约简)是提高识别效率的重要因素。

The selection and extraction of electrocardiogram feature parameter are the base of the analysis of electrocardiogram（ECG）. To improve recognition rate of detection algorithm and classification accuracy is the key to automatic analysis technology. Thus, a hybrid algorithm based on wavelet transform（WT） and attribute reduction of granular computing（GC） to detect premature beat signal of electrocardiogram（ECG） was present. At first, 12 electrocardiogram feature parameters are chosen based on diagnostic criteria from cardiovascular experts. Then the feature detection algo- rithm based on wavelet transform is used for feature extraction, and an attribute reduction algorithm based on granular computing is also used for attribute reductioru Finally,the data are put into pattern classification and the result is verified by MIT-BIH database. As the experiment shows, the classification accuracy after reduction is much higher than it before reduction. Therefore, that reasonable selection of feature parameter is an important factor to improve the recognition efficiency was justified in this article.