{"title":"一种改进的经验模态分解-小波算法心音信号去噪及其在一、二心音提取中的应用","authors":"Haozhou Sun, Wei Chen, Jing-yan Gong","doi":"10.1109/BMEI.2013.6746931","DOIUrl":null,"url":null,"abstract":"In this paper, an improved EMD-Wavelet algorithm for PCG (Phonocardiogram) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. By applying the wavelet transform algorithm and EMD (Empirical Mode Decomposition) for pre-procession, the PCG signal is well filtered. Based on the time frequency domain features of PCG's IMF components which is extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components are pinpointed accurately. Experiments of thirty samples illustrate the proposed algorithm, which reveals that the accuracy for recognition of S1/S2 components is as high as 99.74%.","PeriodicalId":163211,"journal":{"name":"2013 6th International Conference on Biomedical Engineering and Informatics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"An improved empirical mode decomposition-wavelet algorithm for phonocardiogram signal denoising and its application in the first and second heart sound extraction\",\"authors\":\"Haozhou Sun, Wei Chen, Jing-yan Gong\",\"doi\":\"10.1109/BMEI.2013.6746931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an improved EMD-Wavelet algorithm for PCG (Phonocardiogram) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. By applying the wavelet transform algorithm and EMD (Empirical Mode Decomposition) for pre-procession, the PCG signal is well filtered. Based on the time frequency domain features of PCG's IMF components which is extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components are pinpointed accurately. Experiments of thirty samples illustrate the proposed algorithm, which reveals that the accuracy for recognition of S1/S2 components is as high as 99.74%.\",\"PeriodicalId\":163211,\"journal\":{\"name\":\"2013 6th International Conference on Biomedical Engineering and Informatics\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 6th International Conference on Biomedical Engineering and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMEI.2013.6746931\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 6th International Conference on Biomedical Engineering and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEI.2013.6746931","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved empirical mode decomposition-wavelet algorithm for phonocardiogram signal denoising and its application in the first and second heart sound extraction
In this paper, an improved EMD-Wavelet algorithm for PCG (Phonocardiogram) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. By applying the wavelet transform algorithm and EMD (Empirical Mode Decomposition) for pre-procession, the PCG signal is well filtered. Based on the time frequency domain features of PCG's IMF components which is extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components are pinpointed accurately. Experiments of thirty samples illustrate the proposed algorithm, which reveals that the accuracy for recognition of S1/S2 components is as high as 99.74%.