{"title":"基于调制综经验模态分解的心电信号t波识别研究","authors":"Chun-Hsiang Huang, T. Hsiao","doi":"10.1142/S2424922X21500029","DOIUrl":null,"url":null,"abstract":"The cardiovascular diseases are the major cause of death globally. To diagnose heart disease, automatic recognition of ECG’s T-wave is necessary. Empirical mode decomposition (EMD) can be used to decompose nonlinear and nonstationary signals. However, using EMD to decompose ECG potentially leads to a mode mixing problem. This study proposes modulated EEMD (mEEMD) as a solution, which can solve mode mixing problems with almost no influence from noise. Furthermore, the mEEMD has a less problematic boundary side effect and does not cause any phase shift. The sensitivity of T-wave onset and offset recognition is [Formula: see text] and [Formula: see text].","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"40 1","pages":"2150002:1-2150002:29"},"PeriodicalIF":0.5000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Toward T-Wave Recognition of ECG Signals Through Modulated Ensemble Empirical Mode Decomposition\",\"authors\":\"Chun-Hsiang Huang, T. Hsiao\",\"doi\":\"10.1142/S2424922X21500029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The cardiovascular diseases are the major cause of death globally. To diagnose heart disease, automatic recognition of ECG’s T-wave is necessary. Empirical mode decomposition (EMD) can be used to decompose nonlinear and nonstationary signals. However, using EMD to decompose ECG potentially leads to a mode mixing problem. This study proposes modulated EEMD (mEEMD) as a solution, which can solve mode mixing problems with almost no influence from noise. Furthermore, the mEEMD has a less problematic boundary side effect and does not cause any phase shift. The sensitivity of T-wave onset and offset recognition is [Formula: see text] and [Formula: see text].\",\"PeriodicalId\":47145,\"journal\":{\"name\":\"Advances in Data Science and Adaptive Analysis\",\"volume\":\"40 1\",\"pages\":\"2150002:1-2150002:29\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Data Science and Adaptive Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S2424922X21500029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Data Science and Adaptive Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S2424922X21500029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Toward T-Wave Recognition of ECG Signals Through Modulated Ensemble Empirical Mode Decomposition
The cardiovascular diseases are the major cause of death globally. To diagnose heart disease, automatic recognition of ECG’s T-wave is necessary. Empirical mode decomposition (EMD) can be used to decompose nonlinear and nonstationary signals. However, using EMD to decompose ECG potentially leads to a mode mixing problem. This study proposes modulated EEMD (mEEMD) as a solution, which can solve mode mixing problems with almost no influence from noise. Furthermore, the mEEMD has a less problematic boundary side effect and does not cause any phase shift. The sensitivity of T-wave onset and offset recognition is [Formula: see text] and [Formula: see text].