{"title":"基于心周期测定的cnn型心肌梗死预测","authors":"Tsubasa Kanai, N. Tanabe, Y. Miyagi, J. Aoyama","doi":"10.1109/ISPACS51563.2021.9651000","DOIUrl":null,"url":null,"abstract":"This paper proposes the cardiac infarction prediction based on CNN-type myocardial infarction prediction using evaluate of myocardial dynamics. The proposed algorithm (i) remove noise using masking image for the ultrasound images into frame-by-frame, (ii) determine the cardiac cycle using optical flow analysis [1] for the characteristic dynamics of each cardiac anatomical region, and then, (iii) predict the myocardial infarction using CNN from time-frequency analysis of myocardial dynamics.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CNN-type myocardial infarction prediction based on cardiac cycle determination\",\"authors\":\"Tsubasa Kanai, N. Tanabe, Y. Miyagi, J. Aoyama\",\"doi\":\"10.1109/ISPACS51563.2021.9651000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes the cardiac infarction prediction based on CNN-type myocardial infarction prediction using evaluate of myocardial dynamics. The proposed algorithm (i) remove noise using masking image for the ultrasound images into frame-by-frame, (ii) determine the cardiac cycle using optical flow analysis [1] for the characteristic dynamics of each cardiac anatomical region, and then, (iii) predict the myocardial infarction using CNN from time-frequency analysis of myocardial dynamics.\",\"PeriodicalId\":359822,\"journal\":{\"name\":\"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPACS51563.2021.9651000\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS51563.2021.9651000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CNN-type myocardial infarction prediction based on cardiac cycle determination
This paper proposes the cardiac infarction prediction based on CNN-type myocardial infarction prediction using evaluate of myocardial dynamics. The proposed algorithm (i) remove noise using masking image for the ultrasound images into frame-by-frame, (ii) determine the cardiac cycle using optical flow analysis [1] for the characteristic dynamics of each cardiac anatomical region, and then, (iii) predict the myocardial infarction using CNN from time-frequency analysis of myocardial dynamics.