{"title":"心电图t波交替的机器学习技术评价","authors":"O. Karnaukh, Y. Karplyuk, Nataliia Nikitiuk","doi":"10.1109/ELNANO.2018.8477528","DOIUrl":null,"url":null,"abstract":"This paper presents the evaluation of T-wave alternans (TWA) detection based on machine learning techniques. F1-score metric was used for optimal features set selection. KNN, LR, RFC, SVC classifiers were evaluated as a part of TWA detection system. T-criteria was proposed as justification approach to select optimal features number based on classifiers performance. Designed optimal TWA classification system was evaluated on PhysioBank database and classification F1-score about 89% was obtained for RFC classifier.","PeriodicalId":269665,"journal":{"name":"2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Evaluation of Machine Learning Techniques for ECG T-Wave Alternans\",\"authors\":\"O. Karnaukh, Y. Karplyuk, Nataliia Nikitiuk\",\"doi\":\"10.1109/ELNANO.2018.8477528\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the evaluation of T-wave alternans (TWA) detection based on machine learning techniques. F1-score metric was used for optimal features set selection. KNN, LR, RFC, SVC classifiers were evaluated as a part of TWA detection system. T-criteria was proposed as justification approach to select optimal features number based on classifiers performance. Designed optimal TWA classification system was evaluated on PhysioBank database and classification F1-score about 89% was obtained for RFC classifier.\",\"PeriodicalId\":269665,\"journal\":{\"name\":\"2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ELNANO.2018.8477528\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ELNANO.2018.8477528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of Machine Learning Techniques for ECG T-Wave Alternans
This paper presents the evaluation of T-wave alternans (TWA) detection based on machine learning techniques. F1-score metric was used for optimal features set selection. KNN, LR, RFC, SVC classifiers were evaluated as a part of TWA detection system. T-criteria was proposed as justification approach to select optimal features number based on classifiers performance. Designed optimal TWA classification system was evaluated on PhysioBank database and classification F1-score about 89% was obtained for RFC classifier.
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