A PCG classification method using xgboost
Conference: BIBE 2019 - The Third International Conference on Biological Information and Biomedical Engineering
06/20/2019 - 06/22/2019 at Hangzhou, China
Proceedings: BIBE 2019
Pages: 4Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Li, Ting; Chen, Xing-rong; Zhou, Lin; Sun, Fu-ming (School of Information and Communication Engineering, Dalian Minzu University, Dalian, China)
According to the characteristics of PCG, wavelet packet energy features and MFCC features are extracted. The heartbeat signals are classified by xgboost algorithm. The experimental results show that the classification accuracy is significantly higher than that of the single feature when all the features are used. When the training set is 90% and the test set is 10%, the classification results are the best. The accuracy, sensitivity and specificity are 80.31%, 80.01% and 80.61%, respectively.