{"title":"基于波峰的心电生物特征聚类","authors":"M. Milivojević, A. Gavrovska, I. Reljin","doi":"10.1109/NEUREL.2018.8587016","DOIUrl":null,"url":null,"abstract":"The use of ECG signals for biometric recognition is in the focus of scientific research. For each person electrocardiogram which contain specific biometric characteristics can be recorded making it suitable for biometric application. A comparison of features in terms of potential person identification, i.e. clustering needs, is made, where amplitude characteristics are extracted in time domain. This paper analyzes data from the Physionet ECG-ID database, and show promising results for future ECG based considerations.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Biometric Clustering of ECG using Wave Peaks\",\"authors\":\"M. Milivojević, A. Gavrovska, I. Reljin\",\"doi\":\"10.1109/NEUREL.2018.8587016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of ECG signals for biometric recognition is in the focus of scientific research. For each person electrocardiogram which contain specific biometric characteristics can be recorded making it suitable for biometric application. A comparison of features in terms of potential person identification, i.e. clustering needs, is made, where amplitude characteristics are extracted in time domain. This paper analyzes data from the Physionet ECG-ID database, and show promising results for future ECG based considerations.\",\"PeriodicalId\":371831,\"journal\":{\"name\":\"2018 14th Symposium on Neural Networks and Applications (NEUREL)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 14th Symposium on Neural Networks and Applications (NEUREL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2018.8587016\",\"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 14th Symposium on Neural Networks and Applications (NEUREL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2018.8587016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The use of ECG signals for biometric recognition is in the focus of scientific research. For each person electrocardiogram which contain specific biometric characteristics can be recorded making it suitable for biometric application. A comparison of features in terms of potential person identification, i.e. clustering needs, is made, where amplitude characteristics are extracted in time domain. This paper analyzes data from the Physionet ECG-ID database, and show promising results for future ECG based considerations.
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