{"title":"一种心电多分类的集成方法","authors":"Chao-Xin Xie, Minghui Fan, Liang-Hung Wang, Pao-Cheng Huang","doi":"10.1109/ICCE-Taiwan55306.2022.9869291","DOIUrl":null,"url":null,"abstract":"The application of artificial intelligence to the diagnosis of ECG is of great significance. We combine machine learning algorithm with deep learning algorithm to give full play to the advantages of different algorithms by ensemble learning. Finally, we fuse the selected models so that the accuracy of identifying five kinds of arrhythmias can reach 94%. Particularly, the accuracy of class F beat which is difficult to identify has also been improved.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Integration Method for ECG Multi-Classification\",\"authors\":\"Chao-Xin Xie, Minghui Fan, Liang-Hung Wang, Pao-Cheng Huang\",\"doi\":\"10.1109/ICCE-Taiwan55306.2022.9869291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The application of artificial intelligence to the diagnosis of ECG is of great significance. We combine machine learning algorithm with deep learning algorithm to give full play to the advantages of different algorithms by ensemble learning. Finally, we fuse the selected models so that the accuracy of identifying five kinds of arrhythmias can reach 94%. Particularly, the accuracy of class F beat which is difficult to identify has also been improved.\",\"PeriodicalId\":164671,\"journal\":{\"name\":\"2022 IEEE International Conference on Consumer Electronics - Taiwan\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Consumer Electronics - Taiwan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Consumer Electronics - Taiwan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Integration Method for ECG Multi-Classification
The application of artificial intelligence to the diagnosis of ECG is of great significance. We combine machine learning algorithm with deep learning algorithm to give full play to the advantages of different algorithms by ensemble learning. Finally, we fuse the selected models so that the accuracy of identifying five kinds of arrhythmias can reach 94%. Particularly, the accuracy of class F beat which is difficult to identify has also been improved.
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