{"title":"长期动态心电图记录的QRS检测算法","authors":"G. Georgieva-Tsaneva","doi":"10.1145/2516775.2516811","DOIUrl":null,"url":null,"abstract":"In this paper an exact QRS detection algorithm of Holter electrocardiogram signals using Wavelet Transform is present. The orthogonal wavelet functions -- Haar and Daubechies -- is studied and compared. Analysis is carried out using Visual C++ Software. The results show that the described algorithm provides excellent QRS detection even if the signals are contaminated with noises. The presented algorithm is less computationally involved. This makes it ideal for wide range of diagnostic applications, especially for real-time Holter monitoring.","PeriodicalId":316788,"journal":{"name":"International Conference on Computer Systems and Technologies","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"QRS detection algorithm for long term Holter records\",\"authors\":\"G. Georgieva-Tsaneva\",\"doi\":\"10.1145/2516775.2516811\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper an exact QRS detection algorithm of Holter electrocardiogram signals using Wavelet Transform is present. The orthogonal wavelet functions -- Haar and Daubechies -- is studied and compared. Analysis is carried out using Visual C++ Software. The results show that the described algorithm provides excellent QRS detection even if the signals are contaminated with noises. The presented algorithm is less computationally involved. This makes it ideal for wide range of diagnostic applications, especially for real-time Holter monitoring.\",\"PeriodicalId\":316788,\"journal\":{\"name\":\"International Conference on Computer Systems and Technologies\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Computer Systems and Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2516775.2516811\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computer Systems and Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2516775.2516811","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QRS detection algorithm for long term Holter records
In this paper an exact QRS detection algorithm of Holter electrocardiogram signals using Wavelet Transform is present. The orthogonal wavelet functions -- Haar and Daubechies -- is studied and compared. Analysis is carried out using Visual C++ Software. The results show that the described algorithm provides excellent QRS detection even if the signals are contaminated with noises. The presented algorithm is less computationally involved. This makes it ideal for wide range of diagnostic applications, especially for real-time Holter monitoring.