A new diagnosis method for heart valve disease by Using Antibody Memory Clone Clustering Algorithm based on Supervised Gath-Geva algorithm

Yan Wang, Haibin Wang, Lihan Liu, Yu Fang, Jinbao Zhang
2010 2010 3rd International Conference on Computer Science and Information Technology  
In order to discriminate normal and abnormal heart sounds (HSs) accurately and effectively, a new method for clinical diagnosis of the heart valve diseases is proposed. The method is composed of three stages. The first stage is the preprocessing stage. During the pre-processing stage, the improved wavelet threshold shrinkage denoising algorithm is used for the noise reduction of the measured HSs. In the feature extraction stage, the normalized average Shannon energy theorem and wavelet
more » ... are used to extract the time-frequency feature of the HSs. In the classification stage, the proposed Antibody Memory Clone Clustering Algorithm (AMCCA) based on Supervised Gath-Geva algorithm is used. To test the correct classification rate of the proposed method, 110 data (60 normal, 50 abnormal) of the aortic heart valve and 120 data (60 normal, 60 abnormal,) of the mitral heart valve are collected and analyzed, the accuracy performances are achieved by 96.2%, 100%, 96% and 96.4% respectively. Furthermore, Sammon mapping algorithm is used to project the four-dimensional feature data of HSs into a lower twodimensional data to achieve the visualization of the classification results. The experimental results indicate that the proposed method achieves high classification accuracy, and has strong clinical application value.
doi:10.1109/iccsit.2010.5563722 fatcat:gpgfia7r7jfrfleuws6yv34chy