{"title":"Heart Rate Variability Generating Based on Matematical Tools","authors":"G. Georgieva-Tsaneva","doi":"10.1145/3274005.3274035","DOIUrl":null,"url":null,"abstract":"The article presents an algorithm for generating sysnthetic Heart Rate Variability (HRV) data using mathematical tools. The generated data includes the low frequency Mayer wave, the effect of Respiratory Sinus Arrhythmia on the high frequency spectrum and the influence of thermoregulation, physical activity, etc. factors in the very low frequency range. The algorithm uses a wavelet transformation to convert the generated data into the time domain. The generated HRV series has been investigated in the time and frequency domains. The results show that the generated HRV data corresponds to a healthy individual. The algorithm can be used to evaluate the diagnostic capabilities of real HRV sequences derived from patient electrocardiographic data.","PeriodicalId":152033,"journal":{"name":"Proceedings of the 19th International Conference on Computer Systems and Technologies","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th International Conference on Computer Systems and Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3274005.3274035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The article presents an algorithm for generating sysnthetic Heart Rate Variability (HRV) data using mathematical tools. The generated data includes the low frequency Mayer wave, the effect of Respiratory Sinus Arrhythmia on the high frequency spectrum and the influence of thermoregulation, physical activity, etc. factors in the very low frequency range. The algorithm uses a wavelet transformation to convert the generated data into the time domain. The generated HRV series has been investigated in the time and frequency domains. The results show that the generated HRV data corresponds to a healthy individual. The algorithm can be used to evaluate the diagnostic capabilities of real HRV sequences derived from patient electrocardiographic data.