I. Legarreta, P. Addison, N. Grubb, G. Clegg, C. Robertson, J. N. Watson
{"title":"连续小波变换与模极大值分析心电特征的比较","authors":"I. Legarreta, P. Addison, N. Grubb, G. Clegg, C. Robertson, J. N. Watson","doi":"10.1109/CIC.2005.1588214","DOIUrl":null,"url":null,"abstract":"The continuous wavelet transform (CWT) offers a valuable tool for the analysis of signals as it provides precise location in time of high frequency components. The selection of a mother wavelet with high correlation with the signal under study provides a more accurate time-frequency analysis. Continuous wavelet transform modulus maxima (CWTMM) reduce the computational requirement by representing only the pertinent information contained within the scalogram obtained from continuous wavelet analysis. This new domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. The aim of this work was to compare the two time-frequency domains for ECG analysis: CWT and CWTMM, providing example applications of both methods","PeriodicalId":239491,"journal":{"name":"Computers in Cardiology, 2005","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A comparison of continuous wavelet transform and modulus maxima analysis of characteristic ECG features\",\"authors\":\"I. Legarreta, P. Addison, N. Grubb, G. Clegg, C. Robertson, J. N. Watson\",\"doi\":\"10.1109/CIC.2005.1588214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The continuous wavelet transform (CWT) offers a valuable tool for the analysis of signals as it provides precise location in time of high frequency components. The selection of a mother wavelet with high correlation with the signal under study provides a more accurate time-frequency analysis. Continuous wavelet transform modulus maxima (CWTMM) reduce the computational requirement by representing only the pertinent information contained within the scalogram obtained from continuous wavelet analysis. This new domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. The aim of this work was to compare the two time-frequency domains for ECG analysis: CWT and CWTMM, providing example applications of both methods\",\"PeriodicalId\":239491,\"journal\":{\"name\":\"Computers in Cardiology, 2005\",\"volume\":\"76 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in Cardiology, 2005\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIC.2005.1588214\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Cardiology, 2005","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIC.2005.1588214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparison of continuous wavelet transform and modulus maxima analysis of characteristic ECG features
The continuous wavelet transform (CWT) offers a valuable tool for the analysis of signals as it provides precise location in time of high frequency components. The selection of a mother wavelet with high correlation with the signal under study provides a more accurate time-frequency analysis. Continuous wavelet transform modulus maxima (CWTMM) reduce the computational requirement by representing only the pertinent information contained within the scalogram obtained from continuous wavelet analysis. This new domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. The aim of this work was to compare the two time-frequency domains for ECG analysis: CWT and CWTMM, providing example applications of both methods