摘要
心音信号的特征提取和自动识别具有重要的临床意义。为提高正常和异常心音信号识别率,本文首先用DB6小波对心音信号进行降噪处理,然后用希尔伯特-黄变换(HHT)分析提取心音信号的时域、频域特征值,再通过自适应提升小波包提取信号的频带能量特征值,最后通过支持向量机对心音进行分类识别。对临床采集的240例异常心音和正常心音进行实验,正确识别率达到97.2%。可见,希尔伯特-黄变换和自适应提升小波包相结合的方法可有效识别正常和各种异常心音信号。
Heart sound signal feature extraction and automatic identification have important clinical significance. In order to improve recognition rate of normal and abnormal heart sound signal,in this paper, firstly we used DB6 wavelet to reduce noise of the heart sound signal, and then used the hilbert-huang transform （HHT） to extract the time-domain and frequen- cy-domain characteristic values of heart sound signal, and then extracted band energy values through adaptive lifting wavelet packet, and finally classified and recognized heart sound by support vector machine. We experimented to 240 cases of ab- normal heart sounds and normal heart sounds from clinical collection, the results show that recognition rate can reach 97, 2%. It is clear that HHT and adaptive lifting wavelet packet are effective identification means for normal and abnormal heart sound signal.
出处
《信号处理》
CSCD
北大核心
2014年第1期112-118,共7页
Journal of Signal Processing
基金
吉林省科技厅项目基金(20121006)
关键词
心音识别
希尔伯特-黄变换
小波包
特征提取
heart sound identification
hilbert-huang transform
wavelet packet
characteristics extraction
