{"title":"基于CNN的实时ST段抑郁发作单导联检测","authors":"E. Tiryaki, Akshay Sonawane, L. Tamil","doi":"10.1109/ISQED51717.2021.9424275","DOIUrl":null,"url":null,"abstract":"A method for real monitoring of the heart for ST-depression episodes is described here. We have developed a convolutional neural network (CNN) based machine learning algorithm for classifying ECG signals into normal or ST-depression episodes of the heart with an accuracy over 92%. Our algorithm is capable of detecting ST-depression episodes of varying duration. The algorithm is evaluated using European ST-T Database. The best results obtained here are 0.95%, 0.98%, and 0.91% respectively for accuracy, sensitivity, and specificity.","PeriodicalId":123018,"journal":{"name":"2021 22nd International Symposium on Quality Electronic Design (ISQED)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Real-Time CNN Based ST Depression Episode Detection Using Single-Lead ECG\",\"authors\":\"E. Tiryaki, Akshay Sonawane, L. Tamil\",\"doi\":\"10.1109/ISQED51717.2021.9424275\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method for real monitoring of the heart for ST-depression episodes is described here. We have developed a convolutional neural network (CNN) based machine learning algorithm for classifying ECG signals into normal or ST-depression episodes of the heart with an accuracy over 92%. Our algorithm is capable of detecting ST-depression episodes of varying duration. The algorithm is evaluated using European ST-T Database. The best results obtained here are 0.95%, 0.98%, and 0.91% respectively for accuracy, sensitivity, and specificity.\",\"PeriodicalId\":123018,\"journal\":{\"name\":\"2021 22nd International Symposium on Quality Electronic Design (ISQED)\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 22nd International Symposium on Quality Electronic Design (ISQED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISQED51717.2021.9424275\",\"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 22nd International Symposium on Quality Electronic Design (ISQED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISQED51717.2021.9424275","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-Time CNN Based ST Depression Episode Detection Using Single-Lead ECG
A method for real monitoring of the heart for ST-depression episodes is described here. We have developed a convolutional neural network (CNN) based machine learning algorithm for classifying ECG signals into normal or ST-depression episodes of the heart with an accuracy over 92%. Our algorithm is capable of detecting ST-depression episodes of varying duration. The algorithm is evaluated using European ST-T Database. The best results obtained here are 0.95%, 0.98%, and 0.91% respectively for accuracy, sensitivity, and specificity.
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