{"title":"基于特征的心电信号分割","authors":"H. Krim, D.H. Brooks","doi":"10.1109/TFSA.1996.546695","DOIUrl":null,"url":null,"abstract":"Automatic segmentation of ECG signals is important in both clinical and research settings. Past algorithms have relied on incorporation of detailed heuristics. Here, the authors propose a segmentation technique based on the best local trigonometric basis. They show by means of real data examples that the entropy criterion which achieves the most parsimonious representation of a signal results in an overly-fine segmentation of the ECG signal, and thus establish the need for a more comprehensive criterion. The authors introduce a novel best basis search criterion which is based on a linear combination of the entropy measure and a local measure of smoothness and curvature. They tested the algorithm on the MIT-BIH arrythmia database.","PeriodicalId":415923,"journal":{"name":"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Feature-based segmentation of ECG signals\",\"authors\":\"H. Krim, D.H. Brooks\",\"doi\":\"10.1109/TFSA.1996.546695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic segmentation of ECG signals is important in both clinical and research settings. Past algorithms have relied on incorporation of detailed heuristics. Here, the authors propose a segmentation technique based on the best local trigonometric basis. They show by means of real data examples that the entropy criterion which achieves the most parsimonious representation of a signal results in an overly-fine segmentation of the ECG signal, and thus establish the need for a more comprehensive criterion. The authors introduce a novel best basis search criterion which is based on a linear combination of the entropy measure and a local measure of smoothness and curvature. They tested the algorithm on the MIT-BIH arrythmia database.\",\"PeriodicalId\":415923,\"journal\":{\"name\":\"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TFSA.1996.546695\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TFSA.1996.546695","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic segmentation of ECG signals is important in both clinical and research settings. Past algorithms have relied on incorporation of detailed heuristics. Here, the authors propose a segmentation technique based on the best local trigonometric basis. They show by means of real data examples that the entropy criterion which achieves the most parsimonious representation of a signal results in an overly-fine segmentation of the ECG signal, and thus establish the need for a more comprehensive criterion. The authors introduce a novel best basis search criterion which is based on a linear combination of the entropy measure and a local measure of smoothness and curvature. They tested the algorithm on the MIT-BIH arrythmia database.